An Analysis of Multilingual features based Machine Learning Approaches for Sentence Level Sentiment Analysis

Volume: 9    Issue: 2

Year of Publication: 2023

Pages: (15-20)

Authors: Mr. Harjender Singh

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Abstract

Sentiment analysis, a subfield of natural language processing (NLP), involves the automated identification and classification of sentiment or opinion expressed in text. Traditionally, sentiment analysis has focused on English language texts, but with the increasing availability of multilingual data on social media, online reviews, and news articles, there is a growing demand for sentiment analysis in multiple languages. Analyzing sentiment in multiple languages presents unique challenges due to linguistic differences, cultural nuances, and the availability of labeled data. This paper provides an analysis of features based machine learning approaches used for sentiment analysis in multiple languages. It discusses the challenges and considerations specific to multilingual sentiment analysis and provides insights into the performance and effectiveness of different machine learning models. The goal is to explore the performance, effectiveness, and generalization capabilities of different machine learning models across diverse linguistic contexts.

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Keywords

multilingual data, social media, online reviews.