Design an Empirical Framework for Sentiment Analysis from Bangla Text using Machine Learning
| Autor: | Nusrath Tabassum, Muhammad Ibrahim Khan |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
050101 languages & linguistics
Helping hand Computer science business.industry 05 social sciences Sentiment analysis 02 engineering and technology computer.software_genre language.human_language Random forest Support vector machine Bengali Negation 0202 electrical engineering electronic engineering information engineering language 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Artificial intelligence business computer Classifier (UML) Natural language processing Sentence |
| Zdroj: | 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE). |
| Popis: | Natural Language Processing (NLP) lends a helping hand for programming the computers to inspect a huge amount of data. Sentiment analysis is an application of NLP which deals with data to examine the sentiment or opinion that can be either positive or negative. Using Bangla text, sentiment analysis has become a challenge as there were only few works on it. As a decision maker, sentiment extrication not only capturing consumer attitudes but also helps in social behavior observance, politics and policy making. This paper quantifies total positivity and negativity against a document or sentence using Random Forest Classifier to classify sentiments. We contemplate the use of unigram, POS tagging, negation handling and classifier. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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