{"id":542,"date":"2020-09-24T10:58:08","date_gmt":"2020-09-24T17:58:08","guid":{"rendered":"https:\/\/devblogs.microsoft.com\/azure-sdk\/?p=542"},"modified":"2020-10-05T15:22:59","modified_gmt":"2020-10-05T22:22:59","slug":"opinion-mining","status":"publish","type":"post","link":"https:\/\/devblogs.microsoft.com\/azure-sdk\/opinion-mining\/","title":{"rendered":"Opinion Mining with the Text Analytics Client Libraries"},"content":{"rendered":"

With v5.1.0, the new Text Analytics client library now supports opinion mining<\/a>. In this post, we’ll introduce you to the opinion mining concept and then jump into some code.<\/p>\n

Introducing Opinion Mining<\/h2>\n

You can think of opinion mining as a more granular sentiment analysis, diving even deeper into the individual opinions that shape the overall sentiment. More formally, it provides in-depth analysis of opinions about aspects (i.e. attributes) of a product or a service, and is also referred to as Aspect-Based Sentiment Analysis<\/a>.<\/p>\n

Since this is a concept most clearly explained with examples, let’s consider a review (taken directly from the Text Analytics service documentation<\/a>): “The room was great, but the staff was unfriendly”. Returning an overall sentiment for this review doesn’t capture the two distinct opinions of aspects of the hotel, so this is a perfect review to dive deeper into. By using opinion mining, we get that while the “room” is “great”, the “staff” is “unfriendly”. Here is a visual breakdown (also taken directly from the service documentation) of the information opinion mining will extract from this review:<\/p>\n\n\n\n\n\n\n
Aspect<\/th>\nOpinion<\/th>\nSentiment<\/th>\n<\/tr>\n<\/thead>\n
room<\/td>\ngreat<\/td>\npositive<\/td>\n<\/tr>\n
staff<\/td>\nunfriendly<\/td>\nnegative<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Let’s consider a scenario of a hotel manager looking to improve their hotel. This extra layer of opinion mining when conducting sentiment analysis on customer reviews allows them to determine that they should allocate extra resources to train their staff members.<\/p>\n

Other scenarios where opinion mining is useful that aren’t shown in this blog include:<\/p>\n