Abstract
In recent years, due to the availability of voluminous data on web for Indian languages, it has become an important task to analyze this data to retrieve useful information.
Because of the growth of Indian language content, it is beneficial to utilize this explosion of data for the purpose of sentiment analysis.
This research depicts a systematic review in the field of sentiment analysis in general and Indian languages specifically.
The current status of Indian languages in sentiment analysis is classified according to the Indian language families.
The periodical evolution of Indian languages in the field of sentiment analysis, sources of selected publications on the basis of their relevance are also described.
Further, taxonomy of Indian languages in sentiment analysis based on techniques, domains, sentiment levels and classes has been presented.
This research work will assist researchers in finding the available resources such as annotated datasets, pre-processing linguistic and lexical resources in Indian languages for sentiment analysis and will also support in selecting the most suitable sentiment analysis technique in a specific domain along with relevant future research directions.
In case of resource-poor Indian languages with morphological variations, one encounters problems of performing sentiment analysis due to unavailability of annotated resources, linguistic and lexical tools.
Therefore, to provide efficient performance using existing sentiment analysis techniques, the aforementioned issues should be addressed effectively.
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