WHAT IS SENTIMENT ANALYSIS?
Sentiment analysis means understanding the behavior of the subject with respect to some topic. It is also known as OPINION MINING or EMOTION AI. It is a study of various people’s opinions and emotions towards entities, events, actions, and attributes. It uses Text Analytics to my various sources of Data. For Analysis, data is mostly collected from social media sites like Twitter, Facebook. Data is processed in multiple formats from multiple sources to draw out certain conclusions. In the market, there are multiple big data technologies that are equipped with tools that can process and analyze data in various formats.
There are 2 main approaches to analyzing sentiments.
It determines the collective polarity of a document by summing polarities of the individual words.
The analysis is done on a testing dataset which consists of multiple documents which need to be classified.
With strong mathematical optimizations, a model is constructed from training dataset to make decisions.
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There are 3 different levels of sentiment analysis.
The application of Sentiment analysis is broad and powerful. It is mostly used to monitor public thoughts of some topic on social media. The ability to extract insights from social data is a practice that is adopted by many organizations across the globe. It can also be an essential part of one’s market research and customer service approach. The overall experience of the customer is quickly revealed with sentiment analysis. Thus, sentiment analysis is very beneficial to a marketer.
Emotion Mining is a process of extracting emotions from the text. Studying emotions of a group of people for an event occurred helps organizations make promotive decisions. Emotions become an ideal resource for servicing business and decision making. One of its specific directions is TEXT EMOTION MINING, it refers to analyzing people’s emotions based on their writings through observation.
Emotion Mining is broadly classified into 3 categories.
1.Keyword Spotting
2.Lexical Affinity
3.Natural Language Processing
The first category aims to extract the valence of the text. It indicates the polarity of emotions associated with it. The second category aims to determine whether the text is factual or subjective. It determines if the text contains emotions or not. The third category aims to recognize the intensity of emotions in the text.
Thus, it can be concluded that Sentiment analysis and emotion mining help businessmen to understand the public’s choices and preferences on social media and analyzing the data to make key decisions in the business.
Emotion generation and analysis have a number of practical applications including managing customer dealings, human-machine interaction, information retrieval, natural text-to-speech systems, and in social and literary analysis. However, only a limited-coverage on emotion resources exist, and that too only for the English language. Recent research has shown that it is advisable to handle different types of sentences by different strategies. Some specialized tools must be devised to mine particular emotions from variable sources of data which provide precise results. Also, such tools need to be developed that can access and process multilingual data sets. Thus, one can come to the conclusion that Emotion mining is a supported system to sentiment analysis.
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