Extracting Keywords from Data Set and Assigning Priorities For Popularity Analysis Using Classifier Algorithm
| dc.contributor.author | SAHANA P, REDDY | |
| dc.contributor.author | GAGAN REDDY, N | |
| dc.contributor.author | GAGAN VINAY, HEGDE | |
| dc.date.accessioned | 2018-06-19T10:07:22Z | |
| dc.date.available | 2018-06-19T10:07:22Z | |
| dc.date.issued | 2018-06-19T10:07:22Z | |
| dc.description.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% | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/9435 | |
| dc.language.iso | en | en_US |
| dc.subject | SAHANA P REDDY | en_US |
| dc.subject | GAGAN REDDY N | en_US |
| dc.subject | GAGAN VINAY HEGDE | en_US |
| dc.subject | 1NH14CS112 | en_US |
| dc.subject | 1NH14CS741 | en_US |
| dc.subject | 1NH14CS742 | en_US |
| dc.title | Extracting Keywords from Data Set and Assigning Priorities For Popularity Analysis Using Classifier Algorithm | en_US |
| dc.type | Other | en_US |