Extracting Keywords from Data Set and Assigning Priorities For Popularity Analysis Using Classifier Algorithm
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Date
2018-06-19T10:07:22Z
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Abstract
Sentiment analysis or opinion mining is one of the major tasks of NLP (Natural
Language Processing). Sentiment analysis has gained much attention in recent years.
Sentiment is an attitude, thought, or judgment prompted by feeling. Sentiment analysis
which is also known as opinion mining, studies people’s sentiments towards certain
entities. The main aim is to tackle the problem of sentiment polarity categorization, which
is one of the fundamental problems of sentiment analysis. Given a piece of written text,
the problem is to categorize the text into one specific sentiment polarity, positive or
negative.
A general process for sentiment polarity categorization is proposed with detailed
process descriptions. Data used in this study are college reviews collected from
unigo.com. Experiments for both sentence-level categorization and review-level
categorization are performed with promising outcomes. However the data have several
flaws that potentially hinder the process of sentiment analysis. The first flaw is that, since
people can freely post their own content, the quality of their opinions cannot be
guaranteed. The second flaw is that, the ground truth of such data is not always available.
A ground truth is more like a tag of certain opinion, indicating whether the opinion is
positive, negative, or neutral.
There are three levels of sentiment polarity categorization, namely the document
level, the sentence level, and the entity and aspect level. All the sentences were firstly
tokenized into separated English words. The syntactic roles are also known as the parts of
speech. In natural language processing, parts-of-speech (POS) taggers have been
developed to classify words based on their parts of speech. The second process involves
the sentiment score computation for the sentiment tokens. The sentiment score depicts the
level of positivity or negativity of a particular sentiment word. Sentiment tokens and
sentiment scores are information extracted from the original dataset. Then ratings for the
colleges can be made by using those polarities. The efficiency or accuracy that can be achieved using sentiment analysis is about 60-70%
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Keywords
SAHANA P REDDY, GAGAN REDDY N, GAGAN VINAY HEGDE, 1NH14CS112, 1NH14CS741, 1NH14CS742