NDLI: Sentiment analysis and classification based on textual reviews

Content Provider	IEEE Xplore Digital Library
Author	Mouthami, K. Devi, K.N. Bhaskaran, V.M.
Copyright Year	2013
Description	Author affiliation: Dept. of CSE, Kongu Eng. Coll., Erode, India (Mouthami, K.; Devi, K.N.) \|\| Dhirajlal Gandhi Coll. of Technol., Omalur, India (Bhaskaran, V.M.)
Abstract	Mining is used to help people to extract valuable information from large amount of data. Sentiment analysis focuses on the analysis and understanding of the emotions from the text patterns. It identifies the opinion or attitude that a person has towards a topic or an object and it seeks to identify the viewpoint underlying a text span. Sentiment analysis is useful in social media monitoring to automatically characterize the overall feeling or mood of consumers as reflected in social media toward a specific brand or company and determine whether they are viewed positively or negatively on the web. This new form of analysis has been widely adopted in customer relation management especially in the context of complaint management. For automating the task of classifying a single topic textual review, document-level sentiment classification is used for expressing a positive or negative sentiment. So analyzing sentiment using Multi-theme document is very difficult and the accuracy in the classification is less. The document level classification approximately classifies the sentiment using Bag of words in Support Vector Machine (SVM) algorithm. In proposed work, a new algorithm called Sentiment Fuzzy Classification algorithm with parts of speech tags is used to improve the classification accuracy on the benchmark dataset of Movies reviews dataset.
Sponsorship	IEEE Madras Sect.
Starting Page	271
Ending Page	276
File Size	298670
Page Count	6
File Format	PDF
ISBN	9781467357869
e-ISBN	9781467357883
DOI	10.1109/ICICES.2013.6508366
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-02-21
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Sentiment analysis Accuracy Sentiment Fuzzy Classification Text categorization Motion pictures Parts of speech tags Classification algorithms Opinion mining Support Vector Machine Text classification Term weighting
Content Type	Text
Resource Type	Article

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