Book ChapterDOI
Sentiment Classification: An Approach for Indian Language Tweets Using Decision Tree
Sudha Shanker Prasad,Jitendra Kumar,Dinesh Kumar Prabhakar,Sukomal Pal +3 more
- pp 656-663
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TLDR
This paper used a state-of-the-art Data Mining tool Weka to automatically classify the sentiment of Hindi tweets into positive, negative or neutral, with the help of a twitter training dataset in Indian Language Hindi.Abstract:
This paper describes the system we used for Shared Task on Sentiment Analysis in Indian Languages SAIL Tweets, at MIKE-2015. Twitter is one of the most popular platform which allows users to share their opinion in the form of tweets. Since it restricts the users with 140 characters, the tweets are actually very short to carry opinions and sentiments to analyze. We take the help of a twitter training dataset in Indian Language Hindi and apply data mining approaches for analyzing the sentiments. We used a state-of-the-art Data Mining tool Weka to automatically classify the sentiment of Hindi tweets into positive, negative or neutral.read more
Citations
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Journal ArticleDOI
An efficient hybrid filter and evolutionary wrapper approach for sentiment analysis of various topics on Twitter
Mohammad A. Hassonah,Rizik M. H. Al-Sayyed,Ali Rodan,Ali Rodan,Ala' M. Al-Zoubi,Ibrahim Aljarah,Hossam Faris +6 more
TL;DR: This work proposes a hybrid machine learning approach to enhance sentiment analysis; as it builds a classification model based on three classes, which are positive, neutral, and negative emotions, using Support Vector Machines (SVM) classifier, while combining two feature selection techniques using the ReliefF and Multi-Verse Optimizer algorithms.
Journal ArticleDOI
A decision tree using ID3 algorithm for English semantic analysis
TL;DR: A new model is proposed by using an ID3 algorithm of a decision tree to classify semantics (positive, negative, and neutral) for the English documents, and is used in the English document-level emotional classification.
Journal ArticleDOI
A C4.5 algorithm for english emotional classification
TL;DR: A new model using C4.5 Algorithm of a decision tree to classify semantics (positive, negative, neutral) for the English documents, used in the English document-level sentiment classification is proposed.
Journal ArticleDOI
Opinion polarity detection in Twitter data combining shrinkage regression and topic modeling
TL;DR: This study is the first to predict the polarities of public opinion on topics in this manner by automatically detecting polarity in Twitter data using the Lasso and Ridge models of shrinkage regression.
Journal ArticleDOI
Survey on Classic and Latest Textual Sentiment Analysis Articles and Techniques
TL;DR: Text is a typical example of unstructured and heterogeneous data in which massive useful knowledge is embedded and sentiment analysis is used to analyze and predict sentiment polarities of the text.
References
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Book
C4.5: Programs for Machine Learning
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Book
Data Mining: Practical Machine Learning Tools and Techniques
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
Book
Data Mining
TL;DR: In this paper, generalized estimating equations (GEE) with computing using PROC GENMOD in SAS and multilevel analysis of clustered binary data using generalized linear mixed-effects models with PROC LOGISTIC are discussed.
Thumbs up? Sentiment Classiflcation using Machine Learning Techniques
TL;DR: In this paper, the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, was considered and three machine learning methods (Naive Bayes, maximum entropy classiflcation, and support vector machines) were employed.
Proceedings ArticleDOI
Thumbs up? Sentiment Classification using Machine Learning Techniques
TL;DR: This work considers the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, and concludes by examining factors that make the sentiment classification problem more challenging.