Abstract
Sentiment analysis (SA) is used to extract opinions from a huge amount of data and these opinions are comprised of multiple words. Some words have different semantic meanings in different fields and we call them domain specific (DS) words. A domain is defined as a special area in which a collection of queries about a specific topic are held when user do queries in the data regarding the domain appear. But Single word can be interpreted in many ways based on its context-dependency. Demonstrate each word under its domain is extremely important because their meanings differ from each other so much in different domains that a word meaning from A in one context can change into Z in another context or domain. The purpose of this research is to discover the correct sentiment in the message or comment and evaluate it either it is positive, negative or neutral. We collected tweets dataset from different domains and analyze it to extract words that have a different definition in those specific domains as if they are used in other fields of life they would be defined differently. We analyzed 52115 words for finding their DS meaning in seven different domains. Polarity had been given to words of the dataset according to their domains and based on this polarity they have been recognized as positive negative and neutral and evaluated as domain-specific words. The automatic way is used to extract the words of the domain as we integrated and afterward the comparison to identify that either this word differs from other words as far as domain is concerned. This research contribution is a prototype that processes your data and extracts their domain-specific words automatically. This research improved the knowledge about the context-dependency and found the core-specific meanings of words in multiple fields.
Highlights
Sentiment analysis is a somewhat natural language dealing with following the perspective of the all-inclusive community about a particular thing or point
We characterize the polarity of our dataset concerning its domain and compare it with a general language dictionary to extract domain-specific words
We need to state this was the situation with numerous different terms: they were once constrained to specific vocabulary, in any case, as of late, without a doubt because of the worldwide money related circumstance, they have been progressively advancing toward the general dictionary, as they have been frequently utilized as a part of general-group of onlookers media and given uncommon consideration by the overall population, who is presently very comfortable with terms, for example, lodging air pocket or credit crunch
Summary
Sentiment analysis is a somewhat natural language dealing with following the perspective of the all-inclusive community about a particular thing or point. Sentiment analysis, which is known as opinion mining, incorporates into building a system to accumulate and take a gander at suppositions about items made in the blog sections, comments, overviews or tweets. Sentiment analysis can be useful in a couple of ways. The Sentiment analysis structures have focused on specific areas using space-specific corpora as preparing information for the machine learning calculations that organize a data message as either the positive or the negative. Diverse structures are vocabulary-based, where assessment bearing words and articulations are accumulated and a short time later scanned for amid examination to concoct a specific sentiment index
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