Sentiment Analysis : The Language Processing

1689 WordsSep 17, 20167 Pages
Sentiment Analysis Social opinion has been analysed using sentiment analysis (SA). This is basically a natural language processing (NLP) application that uses computational linguistics and text mining to identify text sentiments as positive, negative and neutral. This technique is known as emotional polarity analysis which is related to text mining field, opinion mining and review mining. In addition, to calculate sentiment score, the sentiment acquired from the text is compared to a dictionary in order to determine the strength of that sentiment. Studies on sentiment analysis focus on text written in English such as sentiment lexicons while applying this to other languages will cause domain adaptation problem [12]. Traditional text classification is different from sentiment classification. Traditional text classification refers to pre-defined class to determine a document’s category, and it gauges the theme of the text itself, while the main aim of the latter is to determine the attitudes and opinion through mining and analysing user interest or other subjective information [13]. Studies in sentiment analysis have found that pre-processing the data is the procedure of cleaning and adapting. Data mining discovery is applied to identify patterns in data. Likewise, text mining looks for patterns in text. Text mining can work and analyse the unstructured data such as PDF files, emails and XML files [14]. Many researchers have argued that the use of classifiers in tweets,
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