Language Independent Sentiment Analysis

TL;DR

This paper introduces a language-independent approach for sentiment analysis, enabling effective sentiment detection across multiple languages, thus broadening the applicability of sentiment analytics beyond language-specific methods.

Contribution

It proposes a novel, general method for multilingual sentiment analysis, overcoming the limitations of language-dependent techniques.

Findings

Enables sentiment analysis across multiple languages

Improves applicability of sentiment analytics globally

Facilitates language-oblivious sentiment detection

Abstract

Social media platforms and online forums generate rapid and increasing amount of textual data. Businesses, government agencies, and media organizations seek to perform sentiment analysis on this rich text data. The results of these analytics are used for adapting marketing strategies, customizing products, security and various other decision makings. Sentiment analysis has been extensively studied and various methods have been developed for it with great success. These methods, however apply to texts written in a specific language. This limits applicability to a limited demographic and a specific geographic region. In this paper we propose a general approach for sentiment analysis on data containing texts from multiple languages. This enables all the applications to utilize the results of sentiment analysis in a language oblivious or language-independent fashion.