Sentiment Analysis

Sentiment analysis refers to a method of obtaining information, basically, opinions, of individuals and groups, these can be a group of a particular brand’s customers, etc. Sentimental analysis works on a scoring mechanism, and it evaluates language and voice to collect data about attitudes, opinions and emotions which are related to a business, a product, a service, etc. It can also be called as opinion taking or opinion mining. More significantly, it determines customer’s attitude or opinions. It provides an opportunity to understand the mindset of an individual and study the manner of the product from the opposite point of view. It is a classification algorithm and here, opinion is a very important term. Opinion is partly taken into consideration by considering the objective point of view, but wholly, it is a subjective assessment based on personal experience. An example of sentiment analysis can be social media monitoring, brand monitoring, etc.

There are two main types of sentiment analysis, one being subjectivity or objectivity identification and the other being feature or aspect based identification. Under subjectivity identification, the aim is to find the opinion based data gathered and to identify and classify whether the data is opinionated or not opinionated. Under objectivity identification, the main aim is to identify the objective behind the identification of the opinion being non-subjective.

Feature based identification and determination of different opinions or sentiments which are based more on the opinions and feelings of an individual with respect to a particular product, a business, etc. It involves more of analysing the features of the opinion based identification and how the subject is seen from that angle.

Overall, there are 5 major methods of sentiment analysis, namely, standard sentiment analysis, fine grained sentiment analysis, Emotion detection, aspect based sentiment analysis and intent detection. Each of it will be discussed in brief

Standard sentiment analysis- Being the most popular type of sentiment analysis, it identifies the opinion into three main categories namely, positive, negative and neutral. For example if a person says he loves how a product lets’s ay application is able to take different applications into one application and work together simultaneously on all of those applications, then it is positive sentiment analysis. Likewise, if the person says that he has to test the way the particular application might work, then it is neutral sentiment analysis. And if the person says that the application is very confusing, it means that it is a negative sentiment analysis.

Fine grained sentiment analysis- Fine grained sentiment analysis is an analysis taken from the standard sentiment analysis and is further classified into positive, neutral, negative, very positive and very negative. Example can be the older interface of let’ say a laptop was better or simpler, this is a negative analysis. If the user says that this experience is very awful and that they will never buy, then it will be called as a very negative analysis, and if the user says that there is no particular thing whether what he/she likes or dislikes, then it will be called neutral analysis.

Emotion detection- Emotion detection sentiment analysis is an analysis model which detects the emotions behind the particular words spoken. It tends to connect and associate world’s with emotions such as happiness or sadness or excitement, etc . Example, when a person sends a happy face emoji or the emoticon for happiness by telling that he/she has made let’s say a task more easier for the help taken.

Aspect based detection- Under aspect based detection, the focus is on understanding the aspects or features that are being discussed to get an opinion. Example being any product review given after buying by the customer for other users who tend to see reviews to buy the product.

Intent detection- Under intent detection, one has to find the reason, opinion or intention behind a particular task being performed. Identification of intent detection will help one to ensure that their problems are saved or their grievances are addressed and make improvements based on the reviews given.

Advantages of sentiment analysis: 1) It helps improve customer service

2) It helps in development of products and services which satisfy and delight customers and will fulfill their wants.

3) Helps in developing new and apt marketing strategies.

4)Helps in increasing sales revenue

5)Helps in improving crisis management

Key Benefits of Sentiment Analysis for Businesses

https://callminer.com/blog/sentiment-analysis-examples-best-practices

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