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Balani, Prem
- Rich Semantic Sentiment Analysis Using Lexicon Based Approach
Abstract Views :252 |
PDF Views:3
Authors
Affiliations
1 Department of Information Technology, G.H. Patel College of Engineering and Technology, IN
1 Department of Information Technology, G.H. Patel College of Engineering and Technology, IN
Source
ICTACT Journal on Soft Computing, Vol 7, No 4 (2017), Pagination: 1486-1491Abstract
Web is a huge repository of information, and a massive amount of data is generated everyday on online platforms. Information, can be facts and opinions, facts are objective statements about an event, and opinions are subjective statements that reflect the sentiments of a person towards an event. Research on sentiment analysis has increased tremendously in recent years due to its wide variety of applications. To analyze sentiments, certain methods have been proposed, which can be broadly categorized as supervised machine learning and lexicon based approaches. Supervised machine learning methods are giving high accuracy but these methods need training data and are domain dependent, while lexicon-based methods are not domain dependent. Although, building of lexicon is costly, but once constructed, it can be applied for a wide variety of domains, but still lexicon based methods are restricted to their dictionaries and are full-dependent on the presence of terms that explicitly reflect the sentiment, while in many cases the sentiment of a term is implicitly reflected by the semantics of its context. Therefore, we've proposed context aware, semantically rich (conceptual&contextual semantics) lexicon-based method which is different from traditional lexicon-based methods that assigns sentiment score and strength to terms in a dynamic way, and outperforms baselines.Keywords
Sentiment Analysis, Lexicon, Supervised Machine Learning, Contextual And Conceptual Semantics.
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- A Hybrid Approach for Polarity Shift Detection
Abstract Views :452 |
PDF Views:3
Authors
Affiliations
1 Department of Information Technology, G.H. Patel College of Engineering and Technology, IN
1 Department of Information Technology, G.H. Patel College of Engineering and Technology, IN
Source
ICTACT Journal on Soft Computing, Vol 7, No 4 (2017), Pagination: 1517-1521Abstract
Now-a-days sentiment analysis has become a hot research area. With the increasing use of internet, people express their views by using social media, blogs, etc. So there is a dire need to analyze people's opinions. Sentiment classification is the main task of sentiment analysis. But while classifying sentiments, the problem of polarity shift occurs. Polarity shift is considered as a very crucial problem. Polarity shift changes a text from positive to negative and vice versa. In this paper, a hybrid approach is proposed for polarity shift detection of negation (explicit and implicit) and contrast. The hybrid approach consists of a rule-based approach for detecting explicit negation and contrast and a lexicon called SentiWordNet for detecting implicit negation. The proposed approach outperforms its baselines.Keywords
Sentiment Analysis, Sentiment Classification, Polarity Shift, Natural Language Processing, Lexicon.References
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