Neutrality in the sentiment analysis problem based on fuzzy majority

The problem of data classification is an important topic in the field of machine learning and information retrieval. This has been widely studied and has been applied in many fields. There are multiple models which are proposed for the classification, like tree-structured classifiers, genetic algorithms, Bayesian classification, neural networks etc. These have a large range of applications in different areas like, fraud/spam detection, Customer Segmentation, Medical Diagnosis, Credit approval, weather prediction etc. This project tries to aim at a particular subclass of classification, namely sentiment analysis. Hybrid techniques should be applied in this field of study as each of the existing models have brought about some new expertise and their improvements need to be combined to give higher performance and accuracy. The sentiment analysis problem requires to take as input a block of text and correctly predict the sentiment of the writer or the speaker of the text. We have sufficient data to build a system that uses hybrid techniques like Naïve Bayes and combines the existing models to perform sentiment analysis on a dataset and study its results. The hybrid approach using Naïve Bayes to this problem gives promising results.

Download Full-text

A Survey on Implementation Methods and Applications of Sentiment Analysis

Advances in Business Information Systems and Analytics - Sentiment Analysis and Knowledge Discovery in Contemporary Business ◽

10.4018/978-1-5225-4999-4.ch004 ◽

2019 ◽

pp. 44-58

Author(s):

Sudheer Karnam ◽

Valarmathi B. ◽

Tulasi Prasad Sariki

Keyword(s):

Social Media ◽

Stock Market ◽

Sentiment Analysis ◽

Mobile Phones ◽

Opinion Mining ◽

Classification Methods ◽

Analysis Problem ◽

Public Opinions ◽

The Public ◽

Research Problems

Sentiment analysis also called opinion mining, and it studies opinions of people towards products and services. Opinions are very important as the organizations always want to know the public opinions about their products and services. People give their opinions via social media. With the advent of social media like Twitter, Facebook, blogs, forums, etc. sentiment analysis has become important in every field like automobile, medical, film, fashion, stock market, mobile phones, insurance, etc. Analyzing the opinions and predicting the opinion is called sentiment analysis. Sentiment analysis is done using opinion words by classification methods or by sentiment lexicons. This chapter compares different methods of solving sentiment analysis problem, algorithms, its merits and demerits, applications, and also investigates different research problems in sentiment analysis.

Download Full-text

Study of Sentiment of Governor's Election Opinion in 2018

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset21841124 ◽

2018 ◽

pp. 231-238

Author(s):

Agung Eddy Suryo Saputro ◽

Khairil Anwar Notodiputro ◽

Indahwati A

Keyword(s):

Sentiment Analysis ◽

Naive Bayes ◽

Naïve Bayes ◽

Addition Method ◽

Sentiment Mining ◽

Positive Sentiment ◽

An historical analysis of species references in American English

Corpora ◽

10.3366/cor.2019.0177 ◽

2019 ◽

Vol 14 (3) ◽

pp. 327-349

Author(s):

Craig Frayne

Keyword(s):

Environmental Change ◽

Sentiment Analysis ◽

Quantitative Methods ◽

English Language ◽

Language Use ◽

American English ◽

Historical Analysis ◽

The Past ◽

Corpus Studies ◽

Google Books

This study uses the two largest available American English language corpora, Google Books and the Corpus of Historical American English (coha), to investigate relations between ecology and language. The paper introduces ecolinguistics as a promising theme for corpus research. While some previous ecolinguistic research has used corpus approaches, there is a case to be made for quantitative methods that draw on larger datasets. Building on other corpus studies that have made connections between language use and environmental change, this paper investigates whether linguistic references to other species have changed in the past two centuries and, if so, how. The methodology consists of two main parts: an examination of the frequency of common names of species followed by aspect-level sentiment analysis of concordance lines. Results point to both opportunities and challenges associated with applying corpus methods to ecolinguistc research.

Download Full-text