Proceedings ArticleDOI
Sentiment mining: An approach for Bengali and Tamil tweets
Reads0
Chats0
TLDR
The aim is to classify a given Bengali or Tamil tweets into three sentiment classes namely positive, negative or neutral, using unigram and bi-gram models along with different supervised machine learning techniques.Abstract:
This paper presents a proposed work for extracting the sentiments from tweets in Indian Language. We proposed a system that deal with the goal to extract the sentiments from Bengali & Tamil tweets. Our aim is to classify a given Bengali or Tamil tweets into three sentiment classes namely positive, negative or neutral. In recent time, Twitter gain much attention to NLP researchers as it is most widely used platform that allows the user to share there opinion in form of tweets. The proposed methodology used unigram and bi-gram models along with different supervised machine learning techniques. We also consider the use of features generated from lexical resources such as Wordnets and Emoticons Tagger.read more
Citations
More filters
Proceedings ArticleDOI
Corpus creation for sentiment analysis in code-mixed Tamil-English text
TL;DR: A gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube is created and inter-annotator agreement is presented, and the results of sentiment analysis trained on this corpus are shown.
Posted Content
Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text
TL;DR: In this article, the authors created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube and presented inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.
Book ChapterDOI
BEmoD: Development of Bengali Emotion Dataset for Classifying Expressions of Emotion in Texts
TL;DR: In this article, the authors presented an emotional dataset (hereafter called "BEmoD") for analysis of emotion in Bengali texts and described its development process, including data crawling, pre-processing, labeling, and verification.
Journal ArticleDOI
BEmoC: A Corpus for Identifying Emotion in Bengali Texts
TL;DR: In this article , the authors describe the development of an emotional corpus (hereafter called "BEmoC") for classifying six emotions in Bengali texts, i.e., anger, fear, surprise, sadness, joy, and disgust.
Book ChapterDOI
Indian Language Identification for Short Text
TL;DR: This work classify each line of text to a particular language and focused on short phrases of length 2–6 words for 15 Indian languages to detect that a given document is in multilingual and identifies the appropriate Indian languages.
References
More filters
Subjectivity Detection in English and Bengali: A CRF-based Approach
TL;DR: A relatively simple and less human interactive technique has been proposed for developing opinion mining resources for Bengali and the CRF-based classifier could be extracted for any new language with minimum linguistics knowledge.
Proceedings ArticleDOI
SentiMa - Sentiment extraction for Malayalam
TL;DR: A rule based approach for sentiment analysis from Malayalam movie reviews is proposed, which gives the polarity at the sentence level for the movie reviews with an accuracy of 85%, when analysed.
Book ChapterDOI
Sentiment Classification: An Approach for Indian Language Tweets Using Decision Tree
TL;DR: 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.
Journal ArticleDOI
Verb Based Manipuri Sentiment Analysis
Kishorjit Nongmeikapam,Dilipkumar Khangembam,Wangkheimayum Hemkumar,Shinghajit Khuraijam,Sivaji Bandyopadhyay +4 more
TL;DR: This paper deals about the sentiment analysis of the Manipuri article and the modified lexicon of verbs is modified with the sentiment polarity (Positive or Negative or Neutral) manually.