Language Independent Sentiment Analysis
This addresses the need for businesses, government agencies, and media organizations to analyze sentiment across diverse languages for broader decision-making, though it appears incremental as it builds on existing sentiment analysis methods.
The paper tackles the problem of sentiment analysis being limited to specific languages, which restricts its applicability to certain demographics and regions, by proposing a general approach for performing sentiment analysis on data containing texts from multiple languages, enabling language-independent applications.
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.