diff --git a/sdk/textanalytics/azure-ai-textanalytics/CHANGELOG.md b/sdk/textanalytics/azure-ai-textanalytics/CHANGELOG.md
index 8cbe2435b6d8..be363e8685e5 100644
--- a/sdk/textanalytics/azure-ai-textanalytics/CHANGELOG.md
+++ b/sdk/textanalytics/azure-ai-textanalytics/CHANGELOG.md
@@ -6,6 +6,8 @@
 - We are now targeting the service's v3.1-preview.1 API as the default. If you would like to still use version v3.0 of the service,
 pass in `v3.0` to the kwarg `api_version` when creating your TextAnalyticsClient
 - We have added an API `recognize_pii_entities` which returns entities containing personal information for a batch of documents. Only available for API version v3.1-preview.1 and up.
+- We now have added support for opinion mining. To use this feature, you need to make sure you are using the service's
+v3.1-preview.1 API. To get this support pass `show_opinion_mining` as True when calling the `analyze_sentiment` endpoint
 
 ## 5.0.0 (2020-07-27)
 
diff --git a/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/__init__.py b/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/__init__.py
index e0ac06a728c6..bde288eb0cf6 100644
--- a/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/__init__.py
+++ b/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/__init__.py
@@ -26,8 +26,11 @@
     TextDocumentBatchStatistics,
     SentenceSentiment,
     SentimentConfidenceScores,
+    MinedOpinion,
+    AspectSentiment,
+    OpinionSentiment,
     RecognizePiiEntitiesResult,
-    PiiEntity
+    PiiEntity,
 )
 
 __all__ = [
@@ -51,6 +54,9 @@
     'TextDocumentBatchStatistics',
     'SentenceSentiment',
     'SentimentConfidenceScores',
+    'MinedOpinion',
+    'AspectSentiment',
+    'OpinionSentiment',
     'RecognizePiiEntitiesResult',
     'PiiEntity',
 ]
diff --git a/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_models.py b/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_models.py
index 109c60c5fb95..682ffc9e2a51 100644
--- a/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_models.py
+++ b/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_models.py
@@ -3,10 +3,14 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 # ------------------------------------
+import re
+from ._generated.v3_0.models._models import (
+    LanguageInput,
+    MultiLanguageInput
+)
 
-from ._generated.v3_0.models._models import LanguageInput
-from ._generated.v3_0.models._models import MultiLanguageInput
-
+def _get_indices(relation):
+    return [int(s) for s in re.findall(r"\d+", relation)]
 
 class DictMixin(object):
 
@@ -702,19 +706,34 @@ class SentenceSentiment(DictMixin):
         and 1 for the sentence for all labels.
     :vartype confidence_scores:
         ~azure.ai.textanalytics.SentimentConfidenceScores
+    :ivar mined_opinions: The list of opinions mined from this sentence.
+        For example in "The food is good, but the service is bad", we would
+        mind these two opinions "food is good", "service is bad". Only returned
+        if `show_opinion_mining` is set to True in the call to `analyze_sentiment`.
+    :vartype mined_opinions:
+        list[~azure.ai.textanalytics.MinedOpinion]
     """
 
     def __init__(self, **kwargs):
         self.text = kwargs.get("text", None)
         self.sentiment = kwargs.get("sentiment", None)
         self.confidence_scores = kwargs.get("confidence_scores", None)
+        self.mined_opinions = kwargs.get("mined_opinions", None)
 
     @classmethod
-    def _from_generated(cls, sentence):
+    def _from_generated(cls, sentence, results):
+        if hasattr(sentence, "aspects"):
+            mined_opinions = (
+                [MinedOpinion._from_generated(aspect, results) for aspect in sentence.aspects]  # pylint: disable=protected-access
+                if sentence.aspects else []
+            )
+        else:
+            mined_opinions = None
         return cls(
             text=sentence.text,
             sentiment=sentence.sentiment,
             confidence_scores=SentimentConfidenceScores._from_generated(sentence.confidence_scores),  # pylint: disable=protected-access
+            mined_opinions=mined_opinions
         )
 
     def __repr__(self):
@@ -724,6 +743,150 @@ def __repr__(self):
             repr(self.confidence_scores)
         )[:1024]
 
+class MinedOpinion(DictMixin):
+    """A mined opinion object represents an opinion we've extracted from a sentence.
+    It consists of both an aspect that these opinions are about, and the actual
+    opinions themselves.
+
+    :ivar aspect: The aspect of a product/service that this opinion is about
+    :vartype aspect: ~azure.ai.textanalytics.AspectSentiment
+    :ivar opinions: The actual opinions of the aspect
+    :vartype opinions: list[~azure.ai.textanalytics.OpinionSentiment]
+    """
+
+    def __init__(self, **kwargs):
+        self.aspect = kwargs.get("aspect", None)
+        self.opinions = kwargs.get("opinions", None)
+
+    @staticmethod
+    def _get_opinions(relations, results):
+        if not relations:
+            return []
+        opinion_relations = [r.ref for r in relations if r.relation_type == "opinion"]
+        opinions = []
+        for opinion_relation in opinion_relations:
+            nums = _get_indices(opinion_relation)
+            document_index = nums[0]
+            sentence_index = nums[1]
+            opinion_index = nums[2]
+            opinions.append(
+                results[document_index].sentences[sentence_index].opinions[opinion_index]
+            )
+        return opinions
+
+    @classmethod
+    def _from_generated(cls, aspect, results):
+        return cls(
+            aspect=AspectSentiment._from_generated(aspect),  # pylint: disable=protected-access
+            opinions=[
+                OpinionSentiment._from_generated(opinion) for opinion in cls._get_opinions(aspect.relations, results)  # pylint: disable=protected-access
+            ],
+        )
+
+    def __repr__(self):
+        return "MinedOpinion(aspect={}, opinions={})".format(
+            repr(self.aspect),
+            repr(self.opinions)
+        )[:1024]
+
+
+class AspectSentiment(DictMixin):
+    """AspectSentiment contains the related opinions, predicted sentiment,
+    confidence scores and other information about an aspect of a product.
+    An aspect of a product/service is a key component of that product/service.
+    For example in "The food at Hotel Foo is good", "food" is an aspect of
+    "Hotel Foo".
+
+    :ivar str text: The aspect text.
+    :ivar str sentiment: The predicted Sentiment for the aspect. Possible values
+        include 'positive', 'mixed', and 'negative'.
+    :ivar confidence_scores: The sentiment confidence score between 0
+        and 1 for the aspect for 'positive' and 'negative' labels. It's score
+        for 'neutral' will always be 0
+    :vartype confidence_scores:
+        ~azure.ai.textanalytics.SentimentConfidenceScores
+    :ivar int offset: The aspect offset from the start of the sentence.
+    :ivar int length: The length of the aspect.
+    """
+
+    def __init__(self, **kwargs):
+        self.text = kwargs.get("text", None)
+        self.sentiment = kwargs.get("sentiment", None)
+        self.confidence_scores = kwargs.get("confidence_scores", None)
+        self.offset = kwargs.get("offset", None)
+        self.length = kwargs.get("length", None)
+
+    @classmethod
+    def _from_generated(cls, aspect):
+        return cls(
+            text=aspect.text,
+            sentiment=aspect.sentiment,
+            confidence_scores=SentimentConfidenceScores._from_generated(aspect.confidence_scores),  # pylint: disable=protected-access
+            offset=aspect.offset,
+            length=aspect.length
+        )
+
+    def __repr__(self):
+        return "AspectSentiment(text={}, sentiment={}, confidence_scores={}, offset={}, length={})".format(
+            self.text,
+            self.sentiment,
+            repr(self.confidence_scores),
+            self.offset,
+            self.length
+        )[:1024]
+
+
+class OpinionSentiment(DictMixin):
+    """OpinionSentiment contains the predicted sentiment,
+    confidence scores and other information about an opinion of an aspect.
+    For example, in the sentence "The food is good", the opinion of the
+    aspect 'food' is 'good'.
+
+    :ivar str text: The opinion text.
+    :ivar str sentiment: The predicted Sentiment for the opinion. Possible values
+        include 'positive', 'mixed', and 'negative'.
+    :ivar confidence_scores: The sentiment confidence score between 0
+        and 1 for the opinion for 'positive' and 'negative' labels. It's score
+        for 'neutral' will always be 0
+    :vartype confidence_scores:
+        ~azure.ai.textanalytics.SentimentConfidenceScores
+    :ivar int offset: The opinion offset from the start of the sentence.
+    :ivar int length: The length of the opinion.
+    :ivar bool is_negated: Whether the opinion is negated. For example, in
+        "The food is not good", the opinion "good" is negated.
+    """
+
+    def __init__(self, **kwargs):
+        self.text = kwargs.get("text", None)
+        self.sentiment = kwargs.get("sentiment", None)
+        self.confidence_scores = kwargs.get("confidence_scores", None)
+        self.offset = kwargs.get("offset", None)
+        self.length = kwargs.get("length", None)
+        self.is_negated = kwargs.get("is_negated", None)
+
+    @classmethod
+    def _from_generated(cls, opinion):
+        return cls(
+            text=opinion.text,
+            sentiment=opinion.sentiment,
+            confidence_scores=SentimentConfidenceScores._from_generated(opinion.confidence_scores),  # pylint: disable=protected-access
+            offset=opinion.offset,
+            length=opinion.length,
+            is_negated=opinion.is_negated
+        )
+
+    def __repr__(self):
+        return (
+            "OpinionSentiment(text={}, sentiment={}, confidence_scores={}, offset={}, length={}, is_negated={})".format(
+                self.text,
+                self.sentiment,
+                repr(self.confidence_scores),
+                self.offset,
+                self.length,
+                self.is_negated
+            )[:1024]
+        )
+
 
 class SentimentConfidenceScores(DictMixin):
     """The confidence scores (Softmax scores) between 0 and 1.
@@ -738,15 +901,15 @@ class SentimentConfidenceScores(DictMixin):
     """
 
     def __init__(self, **kwargs):
-        self.positive = kwargs.get('positive', None)
-        self.neutral = kwargs.get('neutral', None)
-        self.negative = kwargs.get('negative', None)
+        self.positive = kwargs.get('positive', 0.0)
+        self.neutral = kwargs.get('neutral', 0.0)
+        self.negative = kwargs.get('negative', 0.0)
 
     @classmethod
     def _from_generated(cls, score):
         return cls(
             positive=score.positive,
-            neutral=score.neutral,
+            neutral=score.neutral if hasattr(score, "netural") else 0.0,
             negative=score.negative
         )
 
diff --git a/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_multiapi.py b/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_multiapi.py
new file mode 100644
index 000000000000..3d29a0c06583
--- /dev/null
+++ b/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_multiapi.py
@@ -0,0 +1,42 @@
+# ------------------------------------
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+# ------------------------------------
+from enum import Enum
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from typing import Union
+
+
+class ApiVersion(str, Enum):
+    """Text Analytics API versions supported by this package"""
+
+    #: this is the default version
+    V3_1_PREVIEW_1 = "v3.1-preview.1"
+    V3_0 = "v3.0"
+
+
+def load_generated_api(api_version, aio=False):
+    try:
+        # api_version could be a string; map it to an instance of ApiVersion
+        # (this is a no-op if it's already an instance of ApiVersion)
+        api_version = ApiVersion(api_version)
+    except ValueError:
+        # api_version is unknown to ApiVersion
+        raise NotImplementedError(
+            "This package doesn't support API version '{}'. ".format(api_version)
+            + "Supported versions: {}".format(", ".join(v.value for v in ApiVersion))
+        )
+
+    if api_version == ApiVersion.V3_1_PREVIEW_1:
+        if aio:
+            from ._generated.v3_1_preview_1.aio import TextAnalyticsClient
+        else:
+            from ._generated.v3_1_preview_1 import TextAnalyticsClient  # type: ignore
+    elif api_version == ApiVersion.V3_0:
+        if aio:
+            from ._generated.v3_0.aio import TextAnalyticsClient  # type: ignore
+        else:
+            from ._generated.v3_0 import TextAnalyticsClient  # type: ignore
+    return TextAnalyticsClient
diff --git a/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_response_handlers.py b/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_response_handlers.py
index 03bf101131fa..94b4593dd769 100644
--- a/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_response_handlers.py
+++ b/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_response_handlers.py
@@ -108,14 +108,14 @@ def wrapper(response, obj, response_headers):  # pylint: disable=unused-argument
             if hasattr(item, "error"):
                 results[idx] = DocumentError(id=item.id, error=TextAnalyticsError._from_generated(item.error))  # pylint: disable=protected-access
             else:
-                results[idx] = func(item)
+                results[idx] = func(item, results)
         return results
 
     return wrapper
 
 
 @prepare_result
-def language_result(language):
+def language_result(language, results):  # pylint: disable=unused-argument
     return DetectLanguageResult(
         id=language.id,
         primary_language=DetectedLanguage._from_generated(language.detected_language),  # pylint: disable=protected-access
@@ -125,7 +125,7 @@ def language_result(language):
 
 
 @prepare_result
-def entities_result(entity):
+def entities_result(entity, results):  # pylint: disable=unused-argument
     return RecognizeEntitiesResult(
         id=entity.id,
         entities=[CategorizedEntity._from_generated(e) for e in entity.entities],  # pylint: disable=protected-access
@@ -135,7 +135,7 @@ def entities_result(entity):
 
 
 @prepare_result
-def linked_entities_result(entity):
+def linked_entities_result(entity, results):  # pylint: disable=unused-argument
     return RecognizeLinkedEntitiesResult(
         id=entity.id,
         entities=[LinkedEntity._from_generated(e) for e in entity.entities],  # pylint: disable=protected-access
@@ -145,7 +145,7 @@ def linked_entities_result(entity):
 
 
 @prepare_result
-def key_phrases_result(phrases):
+def key_phrases_result(phrases, results):  # pylint: disable=unused-argument
     return ExtractKeyPhrasesResult(
         id=phrases.id,
         key_phrases=phrases.key_phrases,
@@ -155,18 +155,18 @@ def key_phrases_result(phrases):
 
 
 @prepare_result
-def sentiment_result(sentiment):
+def sentiment_result(sentiment, results):
     return AnalyzeSentimentResult(
         id=sentiment.id,
         sentiment=sentiment.sentiment,
         warnings=[TextAnalyticsWarning._from_generated(w) for w in sentiment.warnings],  # pylint: disable=protected-access
         statistics=TextDocumentStatistics._from_generated(sentiment.statistics),  # pylint: disable=protected-access
         confidence_scores=SentimentConfidenceScores._from_generated(sentiment.confidence_scores),  # pylint: disable=protected-access
-        sentences=[SentenceSentiment._from_generated(s) for s in sentiment.sentences],  # pylint: disable=protected-access
+        sentences=[SentenceSentiment._from_generated(s, results) for s in sentiment.sentences],  # pylint: disable=protected-access
     )
 
 @prepare_result
-def pii_entities_result(entity):
+def pii_entities_result(entity, results):  # pylint: disable=unused-argument
     return RecognizePiiEntitiesResult(
         id=entity.id,
         entities=[PiiEntity._from_generated(e) for e in entity.entities],  # pylint: disable=protected-access
diff --git a/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_text_analytics_client.py b/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_text_analytics_client.py
index c3a52be62102..c6a89ba98e02 100644
--- a/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_text_analytics_client.py
+++ b/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_text_analytics_client.py
@@ -451,6 +451,12 @@ def analyze_sentiment(  # type: ignore
         :type documents:
             list[str] or list[~azure.ai.textanalytics.TextDocumentInput] or
             list[dict[str, str]]
+        :keyword bool show_opinion_mining: Whether to mine the opinions of a sentence and conduct more
+            granular analysis around the aspects of a product or service (also known as
+            aspect-based sentiment analysis). If set to true, the returned
+            :class:`~azure.ai.textanalytics.SentenceSentiment` objects
+            will have property `mined_opinions` containing the result of this analysis. Only available for
+            API version v3.1-preview.1.
         :keyword str language: The 2 letter ISO 639-1 representation of language for the
             entire batch. For example, use "en" for English; "es" for Spanish etc.
             If not set, uses "en" for English as default. Per-document language will
@@ -460,6 +466,8 @@ def analyze_sentiment(  # type: ignore
             be used for scoring, e.g. "latest", "2019-10-01". If a model-version
             is not specified, the API will default to the latest, non-preview version.
         :keyword bool show_stats: If set to true, response will contain document level statistics.
+        .. versionadded:: v3.1-preview.1
+            The *show_opinion_mining* parameter.
         :return: The combined list of :class:`~azure.ai.textanalytics.AnalyzeSentimentResult` and
             :class:`~azure.ai.textanalytics.DocumentError` in the order the original documents were
             passed in.
@@ -481,6 +489,10 @@ def analyze_sentiment(  # type: ignore
         docs = _validate_batch_input(documents, "language", language)
         model_version = kwargs.pop("model_version", None)
         show_stats = kwargs.pop("show_stats", False)
+        show_opinion_mining = kwargs.pop("show_opinion_mining", None)
+
+        if show_opinion_mining is not None:
+            kwargs.update({"opinion_mining": show_opinion_mining})
         try:
             return self._client.sentiment(
                 documents=docs,
@@ -489,5 +501,11 @@ def analyze_sentiment(  # type: ignore
                 cls=kwargs.pop("cls", sentiment_result),
                 **kwargs
             )
+        except TypeError as error:
+            if "opinion_mining" in str(error):
+                raise NotImplementedError(
+                    "'show_opinion_mining' is only available for API version v3.1-preview.1 and up"
+                )
+            raise error
         except HttpResponseError as error:
             process_batch_error(error)
diff --git a/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/aio/__init__.py b/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/aio/__init__.py
index a3d4ff19e3d8..08ea8acd7812 100644
--- a/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/aio/__init__.py
+++ b/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/aio/__init__.py
@@ -7,5 +7,5 @@
 from ._text_analytics_client_async import TextAnalyticsClient
 
 __all__ = [
-    'TextAnalyticsClient'
+    'TextAnalyticsClient',
 ]
diff --git a/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/aio/_text_analytics_client_async.py b/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/aio/_text_analytics_client_async.py
index 89ac9fc6c943..6d5ea0dd2b67 100644
--- a/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/aio/_text_analytics_client_async.py
+++ b/sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/aio/_text_analytics_client_async.py
@@ -450,6 +450,12 @@ async def analyze_sentiment(  # type: ignore
         :type documents:
             list[str] or list[~azure.ai.textanalytics.TextDocumentInput] or
             list[dict[str, str]]
+        :keyword bool show_opinion_mining: Whether to mine the opinions of a sentence and conduct more
+            granular analysis around the aspects of a product or service (also known as
+            aspect-based sentiment analysis). If set to true, the returned
+            :class:`~azure.ai.textanalytics.SentenceSentiment` objects
+            will have property `mined_opinions` containing the result of this analysis. Only available for
+            API version v3.1-preview.1.
         :keyword str language: The 2 letter ISO 639-1 representation of language for the
             entire batch. For example, use "en" for English; "es" for Spanish etc.
             If not set, uses "en" for English as default. Per-document language will
@@ -459,6 +465,8 @@ async def analyze_sentiment(  # type: ignore
             be used for scoring, e.g. "latest", "2019-10-01". If a model-version
             is not specified, the API will default to the latest, non-preview version.
         :keyword bool show_stats: If set to true, response will contain document level statistics.
+        .. versionadded:: v3.1-preview.1
+            The *show_opinion_mining* parameter.
         :return: The combined list of :class:`~azure.ai.textanalytics.AnalyzeSentimentResult` and
             :class:`~azure.ai.textanalytics.DocumentError` in the order the original documents were
             passed in.
@@ -480,6 +488,11 @@ async def analyze_sentiment(  # type: ignore
         docs = _validate_batch_input(documents, "language", language)
         model_version = kwargs.pop("model_version", None)
         show_stats = kwargs.pop("show_stats", False)
+        show_opinion_mining = kwargs.pop("show_opinion_mining", None)
+
+        if show_opinion_mining is not None:
+            kwargs.update({"opinion_mining": show_opinion_mining})
+
         try:
             return await self._client.sentiment(
                 documents=docs,
@@ -488,5 +501,11 @@ async def analyze_sentiment(  # type: ignore
                 cls=kwargs.pop("cls", sentiment_result),
                 **kwargs
             )
+        except TypeError as error:
+            if "opinion_mining" in str(error):
+                raise NotImplementedError(
+                    "'show_opinion_mining' is only available for API version v3.1-preview.1 and up"
+                )
+            raise error
         except HttpResponseError as error:
             process_batch_error(error)
diff --git a/sdk/textanalytics/azure-ai-textanalytics/tests/recordings/test_analyze_sentiment.test_opinion_mining.yaml b/sdk/textanalytics/azure-ai-textanalytics/tests/recordings/test_analyze_sentiment.test_opinion_mining.yaml
new file mode 100644
index 000000000000..9daa244e8e3b
--- /dev/null
+++ b/sdk/textanalytics/azure-ai-textanalytics/tests/recordings/test_analyze_sentiment.test_opinion_mining.yaml
@@ -0,0 +1,44 @@
+interactions:
+- request:
+    body: '{"documents": [{"id": "0", "text": "It has a sleek premium aluminum design
+      that makes it beautiful to look at.", "language": "en"}]}'
+    headers:
+      Accept:
+      - application/json
+      Accept-Encoding:
+      - gzip, deflate
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '132'
+      Content-Type:
+      - application/json
+      User-Agent:
+      - azsdk-python-ai-textanalytics/5.0.1 Python/3.7.7 (Darwin-17.7.0-x86_64-i386-64bit)
+    method: POST
+    uri: https://westus2.api.cognitive.microsoft.com/text/analytics/v3.1-preview.1/sentiment?showStats=false&opinionMining=true
+  response:
+    body:
+      string: '{"documents":[{"id":"0","sentiment":"positive","confidenceScores":{"positive":0.98,"neutral":0.02,"negative":0.0},"sentences":[{"sentiment":"positive","confidenceScores":{"positive":0.98,"neutral":0.02,"negative":0.0},"offset":0,"length":74,"text":"It
+        has a sleek premium aluminum design that makes it beautiful to look at.","aspects":[{"sentiment":"positive","confidenceScores":{"positive":1.0,"negative":0.0},"offset":32,"length":6,"text":"design","relations":[{"relationType":"opinion","ref":"#/documents/0/sentences/0/opinions/0"},{"relationType":"opinion","ref":"#/documents/0/sentences/0/opinions/1"}]}],"opinions":[{"sentiment":"positive","confidenceScores":{"positive":1.0,"negative":0.0},"offset":9,"length":5,"text":"sleek","isNegated":false},{"sentiment":"positive","confidenceScores":{"positive":1.0,"negative":0.0},"offset":15,"length":7,"text":"premium","isNegated":false}]}],"warnings":[]}],"errors":[],"modelVersion":"2020-04-01"}'
+    headers:
+      apim-request-id:
+      - 4c02e1ce-36d5-4e40-ab94-817af736669d
+      content-type:
+      - application/json; charset=utf-8
+      csp-billing-usage:
+      - CognitiveServices.TextAnalytics.BatchScoring=1
+      date:
+      - Thu, 30 Jul 2020 21:31:42 GMT
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      transfer-encoding:
+      - chunked
+      x-content-type-options:
+      - nosniff
+      x-envoy-upstream-service-time:
+      - '114'
+    status:
+      code: 200
+      message: OK
+version: 1
diff --git a/sdk/textanalytics/azure-ai-textanalytics/tests/recordings/test_analyze_sentiment.test_opinion_mining_no_mined_opinions.yaml b/sdk/textanalytics/azure-ai-textanalytics/tests/recordings/test_analyze_sentiment.test_opinion_mining_no_mined_opinions.yaml
new file mode 100644
index 000000000000..699be403bc2a
--- /dev/null
+++ b/sdk/textanalytics/azure-ai-textanalytics/tests/recordings/test_analyze_sentiment.test_opinion_mining_no_mined_opinions.yaml
@@ -0,0 +1,43 @@
+interactions:
+- request:
+    body: '{"documents": [{"id": "0", "text": "today is a hot day", "language": "en"}]}'
+    headers:
+      Accept:
+      - application/json
+      Accept-Encoding:
+      - gzip, deflate
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '76'
+      Content-Type:
+      - application/json
+      User-Agent:
+      - azsdk-python-ai-textanalytics/5.0.1 Python/3.7.7 (Darwin-17.7.0-x86_64-i386-64bit)
+    method: POST
+    uri: https://westus2.api.cognitive.microsoft.com/text/analytics/v3.1-preview.1/sentiment?showStats=false&opinionMining=true
+  response:
+    body:
+      string: '{"documents":[{"id":"0","sentiment":"neutral","confidenceScores":{"positive":0.1,"neutral":0.88,"negative":0.02},"sentences":[{"sentiment":"neutral","confidenceScores":{"positive":0.1,"neutral":0.88,"negative":0.02},"offset":0,"length":18,"text":"today
+        is a hot day","aspects":[],"opinions":[]}],"warnings":[]}],"errors":[],"modelVersion":"2020-04-01"}'
+    headers:
+      apim-request-id:
+      - 8c5e391d-7ced-4b43-a89b-d87abc04c772
+      content-type:
+      - application/json; charset=utf-8
+      csp-billing-usage:
+      - CognitiveServices.TextAnalytics.BatchScoring=1
+      date:
+      - Thu, 30 Jul 2020 21:38:55 GMT
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      transfer-encoding:
+      - chunked
+      x-content-type-options:
+      - nosniff
+      x-envoy-upstream-service-time:
+      - '90'
+    status:
+      code: 200
+      message: OK
+version: 1
diff --git a/sdk/textanalytics/azure-ai-textanalytics/tests/recordings/test_analyze_sentiment.test_opinion_mining_with_negated_opinion.yaml b/sdk/textanalytics/azure-ai-textanalytics/tests/recordings/test_analyze_sentiment.test_opinion_mining_with_negated_opinion.yaml
new file mode 100644
index 000000000000..a907e92069f7
--- /dev/null
+++ b/sdk/textanalytics/azure-ai-textanalytics/tests/recordings/test_analyze_sentiment.test_opinion_mining_with_negated_opinion.yaml
@@ -0,0 +1,44 @@
+interactions:
+- request:
+    body: '{"documents": [{"id": "0", "text": "The food and service is not good",
+      "language": "en"}]}'
+    headers:
+      Accept:
+      - application/json
+      Accept-Encoding:
+      - gzip, deflate
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '90'
+      Content-Type:
+      - application/json
+      User-Agent:
+      - azsdk-python-ai-textanalytics/5.0.1 Python/3.7.7 (Darwin-17.7.0-x86_64-i386-64bit)
+    method: POST
+    uri: https://westus2.api.cognitive.microsoft.com/text/analytics/v3.1-preview.1/sentiment?showStats=false&opinionMining=true
+  response:
+    body:
+      string: '{"documents":[{"id":"0","sentiment":"negative","confidenceScores":{"positive":0.0,"neutral":0.0,"negative":1.0},"sentences":[{"sentiment":"negative","confidenceScores":{"positive":0.0,"neutral":0.0,"negative":1.0},"offset":0,"length":32,"text":"The
+        food and service is not good","aspects":[{"sentiment":"negative","confidenceScores":{"positive":0.01,"negative":0.99},"offset":4,"length":4,"text":"food","relations":[{"relationType":"opinion","ref":"#/documents/0/sentences/0/opinions/0"}]},{"sentiment":"negative","confidenceScores":{"positive":0.01,"negative":0.99},"offset":13,"length":7,"text":"service","relations":[{"relationType":"opinion","ref":"#/documents/0/sentences/0/opinions/0"}]}],"opinions":[{"sentiment":"negative","confidenceScores":{"positive":0.01,"negative":0.99},"offset":28,"length":4,"text":"good","isNegated":true}]}],"warnings":[]}],"errors":[],"modelVersion":"2020-04-01"}'
+    headers:
+      apim-request-id:
+      - 301fd828-cccc-417e-bd0d-83eeb9dfb542
+      content-type:
+      - application/json; charset=utf-8
+      csp-billing-usage:
+      - CognitiveServices.TextAnalytics.BatchScoring=1
+      date:
+      - Thu, 30 Jul 2020 21:31:42 GMT
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      transfer-encoding:
+      - chunked
+      x-content-type-options:
+      - nosniff
+      x-envoy-upstream-service-time:
+      - '97'
+    status:
+      code: 200
+      message: OK
+version: 1
diff --git a/sdk/textanalytics/azure-ai-textanalytics/tests/recordings/test_analyze_sentiment_async.test_opinion_mining.yaml b/sdk/textanalytics/azure-ai-textanalytics/tests/recordings/test_analyze_sentiment_async.test_opinion_mining.yaml
new file mode 100644
index 000000000000..8bd21eaab0d4
--- /dev/null
+++ b/sdk/textanalytics/azure-ai-textanalytics/tests/recordings/test_analyze_sentiment_async.test_opinion_mining.yaml
@@ -0,0 +1,33 @@
+interactions:
+- request:
+    body: '{"documents": [{"id": "0", "text": "It has a sleek premium aluminum design
+      that makes it beautiful to look at.", "language": "en"}]}'
+    headers:
+      Accept:
+      - application/json
+      Content-Length:
+      - '132'
+      Content-Type:
+      - application/json
+      User-Agent:
+      - azsdk-python-ai-textanalytics/5.0.1 Python/3.7.7 (Darwin-17.7.0-x86_64-i386-64bit)
+    method: POST
+    uri: https://westus2.api.cognitive.microsoft.com/text/analytics/v3.1-preview.1/sentiment?showStats=false&opinionMining=true
+  response:
+    body:
+      string: '{"documents":[{"id":"0","sentiment":"positive","confidenceScores":{"positive":0.98,"neutral":0.02,"negative":0.0},"sentences":[{"sentiment":"positive","confidenceScores":{"positive":0.98,"neutral":0.02,"negative":0.0},"offset":0,"length":74,"text":"It
+        has a sleek premium aluminum design that makes it beautiful to look at.","aspects":[{"sentiment":"positive","confidenceScores":{"positive":1.0,"negative":0.0},"offset":32,"length":6,"text":"design","relations":[{"relationType":"opinion","ref":"#/documents/0/sentences/0/opinions/0"},{"relationType":"opinion","ref":"#/documents/0/sentences/0/opinions/1"}]}],"opinions":[{"sentiment":"positive","confidenceScores":{"positive":1.0,"negative":0.0},"offset":9,"length":5,"text":"sleek","isNegated":false},{"sentiment":"positive","confidenceScores":{"positive":1.0,"negative":0.0},"offset":15,"length":7,"text":"premium","isNegated":false}]}],"warnings":[]}],"errors":[],"modelVersion":"2020-04-01"}'
+    headers:
+      apim-request-id: 6576433c-7362-4637-804c-a25189ff691d
+      content-type: application/json; charset=utf-8
+      csp-billing-usage: CognitiveServices.TextAnalytics.BatchScoring=1
+      date: Thu, 30 Jul 2020 21:31:43 GMT
+      strict-transport-security: max-age=31536000; includeSubDomains; preload
+      transfer-encoding: chunked
+      x-content-type-options: nosniff
+      x-envoy-upstream-service-time: '96'
+    status:
+      code: 200
+      message: OK
+    url: https://westus2.api.cognitive.microsoft.com//text/analytics/v3.1-preview.1/sentiment?showStats=false&opinionMining=true
+version: 1
diff --git a/sdk/textanalytics/azure-ai-textanalytics/tests/recordings/test_analyze_sentiment_async.test_opinion_mining_no_mined_opinions.yaml b/sdk/textanalytics/azure-ai-textanalytics/tests/recordings/test_analyze_sentiment_async.test_opinion_mining_no_mined_opinions.yaml
new file mode 100644
index 000000000000..882cb8156f35
--- /dev/null
+++ b/sdk/textanalytics/azure-ai-textanalytics/tests/recordings/test_analyze_sentiment_async.test_opinion_mining_no_mined_opinions.yaml
@@ -0,0 +1,32 @@
+interactions:
+- request:
+    body: '{"documents": [{"id": "0", "text": "today is a hot day", "language": "en"}]}'
+    headers:
+      Accept:
+      - application/json
+      Content-Length:
+      - '76'
+      Content-Type:
+      - application/json
+      User-Agent:
+      - azsdk-python-ai-textanalytics/5.0.1 Python/3.7.7 (Darwin-17.7.0-x86_64-i386-64bit)
+    method: POST
+    uri: https://westus2.api.cognitive.microsoft.com/text/analytics/v3.1-preview.1/sentiment?showStats=false&opinionMining=true
+  response:
+    body:
+      string: '{"documents":[{"id":"0","sentiment":"neutral","confidenceScores":{"positive":0.1,"neutral":0.88,"negative":0.02},"sentences":[{"sentiment":"neutral","confidenceScores":{"positive":0.1,"neutral":0.88,"negative":0.02},"offset":0,"length":18,"text":"today
+        is a hot day","aspects":[],"opinions":[]}],"warnings":[]}],"errors":[],"modelVersion":"2020-04-01"}'
+    headers:
+      apim-request-id: feb1be87-502c-4e43-b981-c7fb7e6d2b30
+      content-type: application/json; charset=utf-8
+      csp-billing-usage: CognitiveServices.TextAnalytics.BatchScoring=1
+      date: Thu, 30 Jul 2020 21:38:56 GMT
+      strict-transport-security: max-age=31536000; includeSubDomains; preload
+      transfer-encoding: chunked
+      x-content-type-options: nosniff
+      x-envoy-upstream-service-time: '128'
+    status:
+      code: 200
+      message: OK
+    url: https://westus2.api.cognitive.microsoft.com//text/analytics/v3.1-preview.1/sentiment?showStats=false&opinionMining=true
+version: 1
diff --git a/sdk/textanalytics/azure-ai-textanalytics/tests/recordings/test_analyze_sentiment_async.test_opinion_mining_with_negated_opinion.yaml b/sdk/textanalytics/azure-ai-textanalytics/tests/recordings/test_analyze_sentiment_async.test_opinion_mining_with_negated_opinion.yaml
new file mode 100644
index 000000000000..240f3545c987
--- /dev/null
+++ b/sdk/textanalytics/azure-ai-textanalytics/tests/recordings/test_analyze_sentiment_async.test_opinion_mining_with_negated_opinion.yaml
@@ -0,0 +1,33 @@
+interactions:
+- request:
+    body: '{"documents": [{"id": "0", "text": "The food and service is not good",
+      "language": "en"}]}'
+    headers:
+      Accept:
+      - application/json
+      Content-Length:
+      - '90'
+      Content-Type:
+      - application/json
+      User-Agent:
+      - azsdk-python-ai-textanalytics/5.0.1 Python/3.7.7 (Darwin-17.7.0-x86_64-i386-64bit)
+    method: POST
+    uri: https://westus2.api.cognitive.microsoft.com/text/analytics/v3.1-preview.1/sentiment?showStats=false&opinionMining=true
+  response:
+    body:
+      string: '{"documents":[{"id":"0","sentiment":"negative","confidenceScores":{"positive":0.0,"neutral":0.0,"negative":1.0},"sentences":[{"sentiment":"negative","confidenceScores":{"positive":0.0,"neutral":0.0,"negative":1.0},"offset":0,"length":32,"text":"The
+        food and service is not good","aspects":[{"sentiment":"negative","confidenceScores":{"positive":0.01,"negative":0.99},"offset":4,"length":4,"text":"food","relations":[{"relationType":"opinion","ref":"#/documents/0/sentences/0/opinions/0"}]},{"sentiment":"negative","confidenceScores":{"positive":0.01,"negative":0.99},"offset":13,"length":7,"text":"service","relations":[{"relationType":"opinion","ref":"#/documents/0/sentences/0/opinions/0"}]}],"opinions":[{"sentiment":"negative","confidenceScores":{"positive":0.01,"negative":0.99},"offset":28,"length":4,"text":"good","isNegated":true}]}],"warnings":[]}],"errors":[],"modelVersion":"2020-04-01"}'
+    headers:
+      apim-request-id: c2bf1989-2526-45db-a201-02972303c315
+      content-type: application/json; charset=utf-8
+      csp-billing-usage: CognitiveServices.TextAnalytics.BatchScoring=1
+      date: Thu, 30 Jul 2020 21:31:43 GMT
+      strict-transport-security: max-age=31536000; includeSubDomains; preload
+      transfer-encoding: chunked
+      x-content-type-options: nosniff
+      x-envoy-upstream-service-time: '80'
+    status:
+      code: 200
+      message: OK
+    url: https://westus2.api.cognitive.microsoft.com//text/analytics/v3.1-preview.1/sentiment?showStats=false&opinionMining=true
+version: 1
diff --git a/sdk/textanalytics/azure-ai-textanalytics/tests/test_analyze_sentiment.py b/sdk/textanalytics/azure-ai-textanalytics/tests/test_analyze_sentiment.py
index ff1b06f49e71..86c0681c2966 100644
--- a/sdk/textanalytics/azure-ai-textanalytics/tests/test_analyze_sentiment.py
+++ b/sdk/textanalytics/azure-ai-textanalytics/tests/test_analyze_sentiment.py
@@ -15,7 +15,8 @@
 from azure.ai.textanalytics import (
     TextAnalyticsClient,
     TextDocumentInput,
-    VERSION
+    VERSION,
+    ApiVersion
 )
 
 # pre-apply the client_cls positional argument so it needn't be explicitly passed below
@@ -573,3 +574,100 @@ def callback(pipeline_response, deserialized, _):
             cls=callback
         )
         assert res == "cls result"
+
+    @GlobalTextAnalyticsAccountPreparer()
+    @TextAnalyticsClientPreparer()
+    def test_opinion_mining(self, client):
+        documents = [
+            "It has a sleek premium aluminum design that makes it beautiful to look at."
+        ]
+
+        document = client.analyze_sentiment(documents=documents, show_opinion_mining=True)[0]
+
+        for sentence in document.sentences:
+            for mined_opinion in sentence.mined_opinions:
+                aspect = mined_opinion.aspect
+                self.assertEqual('design', aspect.text)
+                self.assertEqual('positive', aspect.sentiment)
+                self.assertIsNotNone(aspect.confidence_scores.positive)
+                self.assertEqual(0.0, aspect.confidence_scores.neutral)
+                self.assertIsNotNone(aspect.confidence_scores.negative)
+                self.assertEqual(32, aspect.offset)
+                self.assertEqual(6, aspect.length)
+
+                sleek_opinion = mined_opinion.opinions[0]
+                self.assertEqual('sleek', sleek_opinion.text)
+                self.assertEqual('positive', sleek_opinion.sentiment)
+                self.assertIsNotNone(sleek_opinion.confidence_scores.positive)
+                self.assertEqual(0.0, sleek_opinion.confidence_scores.neutral)
+                self.assertIsNotNone(sleek_opinion.confidence_scores.negative)
+                self.assertEqual(9, sleek_opinion.offset)
+                self.assertEqual(5, sleek_opinion.length)
+                self.assertFalse(sleek_opinion.is_negated)
+
+                premium_opinion = mined_opinion.opinions[1]
+                self.assertEqual('premium', premium_opinion.text)
+                self.assertEqual('positive', premium_opinion.sentiment)
+                self.assertIsNotNone(premium_opinion.confidence_scores.positive)
+                self.assertEqual(0.0, premium_opinion.confidence_scores.neutral)
+                self.assertIsNotNone(premium_opinion.confidence_scores.negative)
+                self.assertEqual(15, premium_opinion.offset)
+                self.assertEqual(7, premium_opinion.length)
+                self.assertFalse(premium_opinion.is_negated)
+
+    @GlobalTextAnalyticsAccountPreparer()
+    @TextAnalyticsClientPreparer()
+    def test_opinion_mining_with_negated_opinion(self, client):
+        documents = [
+            "The food and service is not good"
+        ]
+
+        document = client.analyze_sentiment(documents=documents, show_opinion_mining=True)[0]
+
+        for sentence in document.sentences:
+            food_aspect = sentence.mined_opinions[0].aspect
+            service_aspect = sentence.mined_opinions[1].aspect
+
+            self.assertEqual('food', food_aspect.text)
+            self.assertEqual('negative', food_aspect.sentiment)
+            self.assertIsNotNone(food_aspect.confidence_scores.positive)
+            self.assertEqual(0.0, food_aspect.confidence_scores.neutral)
+            self.assertIsNotNone(food_aspect.confidence_scores.negative)
+            self.assertEqual(4, food_aspect.offset)
+            self.assertEqual(4, food_aspect.length)
+
+            self.assertEqual('service', service_aspect.text)
+            self.assertEqual('negative', service_aspect.sentiment)
+            self.assertIsNotNone(service_aspect.confidence_scores.positive)
+            self.assertEqual(0.0, service_aspect.confidence_scores.neutral)
+            self.assertIsNotNone(service_aspect.confidence_scores.negative)
+            self.assertEqual(13, service_aspect.offset)
+            self.assertEqual(7, service_aspect.length)
+
+            food_opinion = sentence.mined_opinions[0].opinions[0]
+            service_opinion = sentence.mined_opinions[1].opinions[0]
+            self.assertOpinionsEqual(food_opinion, service_opinion)
+
+            self.assertEqual('good', food_opinion.text)
+            self.assertEqual('negative', food_opinion.sentiment)
+            self.assertIsNotNone(food_opinion.confidence_scores.positive)
+            self.assertEqual(0.0, food_opinion.confidence_scores.neutral)
+            self.assertIsNotNone(food_opinion.confidence_scores.negative)
+            self.assertEqual(28, food_opinion.offset)
+            self.assertEqual(4, food_opinion.length)
+            self.assertTrue(food_opinion.is_negated)
+
+    @GlobalTextAnalyticsAccountPreparer()
+    @TextAnalyticsClientPreparer()
+    def test_opinion_mining_no_mined_opinions(self, client):
+        document = client.analyze_sentiment(documents=["today is a hot day"], show_opinion_mining=True)[0]
+
+        assert not document.sentences[0].mined_opinions
+
+    @GlobalTextAnalyticsAccountPreparer()
+    @TextAnalyticsClientPreparer(client_kwargs={"api_version": ApiVersion.V3_0})
+    def test_opinion_mining_v3(self, client):
+        with pytest.raises(NotImplementedError) as excinfo:
+            client.analyze_sentiment(["will fail"], show_opinion_mining=True)
+
+        assert "'show_opinion_mining' is only available for API version v3.1-preview.1 and up" in str(excinfo.value)
diff --git a/sdk/textanalytics/azure-ai-textanalytics/tests/test_analyze_sentiment_async.py b/sdk/textanalytics/azure-ai-textanalytics/tests/test_analyze_sentiment_async.py
index 48b36ae9a055..b6151ac14ff9 100644
--- a/sdk/textanalytics/azure-ai-textanalytics/tests/test_analyze_sentiment_async.py
+++ b/sdk/textanalytics/azure-ai-textanalytics/tests/test_analyze_sentiment_async.py
@@ -16,7 +16,8 @@
 from azure.ai.textanalytics import (
     VERSION,
     DetectLanguageInput,
-    TextDocumentInput
+    TextDocumentInput,
+    ApiVersion
 )
 
 from testcase import GlobalTextAnalyticsAccountPreparer
@@ -589,3 +590,100 @@ def callback(pipeline_response, deserialized, _):
             cls=callback
         )
         assert res == "cls result"
+
+    @GlobalTextAnalyticsAccountPreparer()
+    @TextAnalyticsClientPreparer()
+    async def test_opinion_mining(self, client):
+        documents = [
+            "It has a sleek premium aluminum design that makes it beautiful to look at."
+        ]
+
+        document = (await client.analyze_sentiment(documents=documents, show_opinion_mining=True))[0]
+
+        for sentence in document.sentences:
+            for mined_opinion in sentence.mined_opinions:
+                aspect = mined_opinion.aspect
+                self.assertEqual('design', aspect.text)
+                self.assertEqual('positive', aspect.sentiment)
+                self.assertIsNotNone(aspect.confidence_scores.positive)
+                self.assertEqual(0.0, aspect.confidence_scores.neutral)
+                self.assertIsNotNone(aspect.confidence_scores.negative)
+                self.assertEqual(32, aspect.offset)
+                self.assertEqual(6, aspect.length)
+
+                sleek_opinion = mined_opinion.opinions[0]
+                self.assertEqual('sleek', sleek_opinion.text)
+                self.assertEqual('positive', sleek_opinion.sentiment)
+                self.assertIsNotNone(sleek_opinion.confidence_scores.positive)
+                self.assertEqual(0.0, sleek_opinion.confidence_scores.neutral)
+                self.assertIsNotNone(sleek_opinion.confidence_scores.negative)
+                self.assertEqual(9, sleek_opinion.offset)
+                self.assertEqual(5, sleek_opinion.length)
+                self.assertFalse(sleek_opinion.is_negated)
+
+                premium_opinion = mined_opinion.opinions[1]
+                self.assertEqual('premium', premium_opinion.text)
+                self.assertEqual('positive', premium_opinion.sentiment)
+                self.assertIsNotNone(premium_opinion.confidence_scores.positive)
+                self.assertEqual(0.0, premium_opinion.confidence_scores.neutral)
+                self.assertIsNotNone(premium_opinion.confidence_scores.negative)
+                self.assertEqual(15, premium_opinion.offset)
+                self.assertEqual(7, premium_opinion.length)
+                self.assertFalse(premium_opinion.is_negated)
+
+    @GlobalTextAnalyticsAccountPreparer()
+    @TextAnalyticsClientPreparer()
+    async def test_opinion_mining_with_negated_opinion(self, client):
+        documents = [
+            "The food and service is not good"
+        ]
+
+        document = (await client.analyze_sentiment(documents=documents, show_opinion_mining=True))[0]
+
+        for sentence in document.sentences:
+            food_aspect = sentence.mined_opinions[0].aspect
+            service_aspect = sentence.mined_opinions[1].aspect
+
+            self.assertEqual('food', food_aspect.text)
+            self.assertEqual('negative', food_aspect.sentiment)
+            self.assertIsNotNone(food_aspect.confidence_scores.positive)
+            self.assertEqual(0.0, food_aspect.confidence_scores.neutral)
+            self.assertIsNotNone(food_aspect.confidence_scores.negative)
+            self.assertEqual(4, food_aspect.offset)
+            self.assertEqual(4, food_aspect.length)
+
+            self.assertEqual('service', service_aspect.text)
+            self.assertEqual('negative', service_aspect.sentiment)
+            self.assertIsNotNone(service_aspect.confidence_scores.positive)
+            self.assertEqual(0.0, service_aspect.confidence_scores.neutral)
+            self.assertIsNotNone(service_aspect.confidence_scores.negative)
+            self.assertEqual(13, service_aspect.offset)
+            self.assertEqual(7, service_aspect.length)
+
+            food_opinion = sentence.mined_opinions[0].opinions[0]
+            service_opinion = sentence.mined_opinions[1].opinions[0]
+            self.assertOpinionsEqual(food_opinion, service_opinion)
+
+            self.assertEqual('good', food_opinion.text)
+            self.assertEqual('negative', food_opinion.sentiment)
+            self.assertIsNotNone(food_opinion.confidence_scores.positive)
+            self.assertEqual(0.0, food_opinion.confidence_scores.neutral)
+            self.assertIsNotNone(food_opinion.confidence_scores.negative)
+            self.assertEqual(28, food_opinion.offset)
+            self.assertEqual(4, food_opinion.length)
+            self.assertTrue(food_opinion.is_negated)
+
+    @GlobalTextAnalyticsAccountPreparer()
+    @TextAnalyticsClientPreparer()
+    async def test_opinion_mining_no_mined_opinions(self, client):
+        document = (await client.analyze_sentiment(documents=["today is a hot day"], show_opinion_mining=True))[0]
+
+        assert not document.sentences[0].mined_opinions
+
+    @GlobalTextAnalyticsAccountPreparer()
+    @TextAnalyticsClientPreparer(client_kwargs={"api_version": ApiVersion.V3_0})
+    async def test_opinion_mining_v3(self, client):
+        with pytest.raises(NotImplementedError) as excinfo:
+            await client.analyze_sentiment(["will fail"], show_opinion_mining=True)
+
+        assert "'show_opinion_mining' is only available for API version v3.1-preview.1 and up" in str(excinfo.value)
diff --git a/sdk/textanalytics/azure-ai-textanalytics/tests/test_multiapi_async.py b/sdk/textanalytics/azure-ai-textanalytics/tests/test_multiapi_async.py
index 830072716808..1e56ad93d590 100644
--- a/sdk/textanalytics/azure-ai-textanalytics/tests/test_multiapi_async.py
+++ b/sdk/textanalytics/azure-ai-textanalytics/tests/test_multiapi_async.py
@@ -4,7 +4,6 @@
 # Licensed under the MIT License.
 # ------------------------------------
 import functools
-from azure.core.credentials import AzureKeyCredential
 from azure.ai.textanalytics import ApiVersion
 from azure.ai.textanalytics.aio import TextAnalyticsClient
 from testcase import TextAnalyticsTest, GlobalTextAnalyticsAccountPreparer
diff --git a/sdk/textanalytics/azure-ai-textanalytics/tests/test_text_analytics.py b/sdk/textanalytics/azure-ai-textanalytics/tests/test_text_analytics.py
index d49a4a0cc3e4..7d3ea071ff5c 100644
--- a/sdk/textanalytics/azure-ai-textanalytics/tests/test_text_analytics.py
+++ b/sdk/textanalytics/azure-ai-textanalytics/tests/test_text_analytics.py
@@ -120,6 +120,30 @@ def test_repr(self):
             transaction_count=4
         )
 
+        aspect_opinion_confidence_score = _models.SentimentConfidenceScores(positive=0.5, negative=0.5)
+
+        opinion_sentiment = _models.OpinionSentiment(
+            text="opinion",
+            sentiment="positive",
+            confidence_scores=aspect_opinion_confidence_score,
+            offset=3,
+            length=7,
+            is_negated=False
+        )
+
+        aspect_sentiment = _models.AspectSentiment(
+            text="aspect",
+            sentiment="positive",
+            confidence_scores=aspect_opinion_confidence_score,
+            offset=10,
+            length=6
+        )
+
+        mined_opinion = _models.MinedOpinion(
+            aspect=aspect_sentiment,
+            opinions=[opinion_sentiment]
+        )
+
         self.assertEqual("DetectedLanguage(name=English, iso6391_name=en, confidence_score=1.0)", repr(detected_language))
         self.assertEqual("CategorizedEntity(text=Bill Gates, category=Person, subcategory=Age, confidence_score=0.899)",
                          repr(categorized_entity))
@@ -186,6 +210,20 @@ def test_repr(self):
         self.assertEqual("TextDocumentBatchStatistics(document_count=1, valid_document_count=2, "
                          "erroneous_document_count=3, transaction_count=4)", repr(text_document_batch_statistics))
 
+        opinion_sentiment_repr = (
+            "OpinionSentiment(text=opinion, sentiment=positive, confidence_scores=SentimentConfidenceScores("
+            "positive=0.5, neutral=0.0, negative=0.5), offset=3, length=7, is_negated=False)"
+        )
+        self.assertEqual(opinion_sentiment_repr, repr(opinion_sentiment))
+
+        aspect_sentiment_repr = (
+            "AspectSentiment(text=aspect, sentiment=positive, confidence_scores=SentimentConfidenceScores("
+            "positive=0.5, neutral=0.0, negative=0.5), offset=10, length=6)"
+        )
+        self.assertEqual(aspect_sentiment_repr, repr(aspect_sentiment))
+        self.assertEqual("MinedOpinion(aspect={}, opinions=[{}])".format(aspect_sentiment_repr, opinion_sentiment_repr), repr(mined_opinion))
+
+
     def test_inner_error_takes_precedence(self):
         generated_innererror = _generated_models.InnerError(
             code="UnsupportedLanguageCode",
diff --git a/sdk/textanalytics/azure-ai-textanalytics/tests/test_unittests.py b/sdk/textanalytics/azure-ai-textanalytics/tests/test_unittests.py
new file mode 100644
index 000000000000..1ec9d72224eb
--- /dev/null
+++ b/sdk/textanalytics/azure-ai-textanalytics/tests/test_unittests.py
@@ -0,0 +1,14 @@
+# coding=utf-8
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for
+# license information.
+# --------------------------------------------------------------------------
+from azure.ai.textanalytics._models import _get_indices
+from testcase import TextAnalyticsTest
+
+
+class TestUnittests(TextAnalyticsTest):
+
+    def test_json_pointer_parsing(self):
+        assert [1, 0, 15] == _get_indices("#/documents/1/sentences/0/opinions/15")
diff --git a/sdk/textanalytics/azure-ai-textanalytics/tests/testcase.py b/sdk/textanalytics/azure-ai-textanalytics/tests/testcase.py
index 483d63cabcd3..d8adc379b490 100644
--- a/sdk/textanalytics/azure-ai-textanalytics/tests/testcase.py
+++ b/sdk/textanalytics/azure-ai-textanalytics/tests/testcase.py
@@ -51,6 +51,16 @@ def generate_oauth_token(self):
     def generate_fake_token(self):
         return FakeTokenCredential()
 
+    def assertOpinionsEqual(self, opinion_one, opinion_two):
+        self.assertEqual(opinion_one.sentiment, opinion_two.sentiment)
+        self.assertEqual(opinion_one.confidence_scores.positive, opinion_two.confidence_scores.positive)
+        self.assertEqual(opinion_one.confidence_scores.neutral, opinion_two.confidence_scores.neutral)
+        self.assertEqual(opinion_one.confidence_scores.negative, opinion_two.confidence_scores.negative)
+        self.assertEqual(opinion_one.offset, opinion_two.offset)
+        self.assertEqual(opinion_one.length, opinion_two.length)
+        self.assertEqual(opinion_one.text, opinion_two.text)
+        self.assertEqual(opinion_one.is_negated, opinion_two.is_negated)
+
 
 class GlobalResourceGroupPreparer(AzureMgmtPreparer):
     def __init__(self):