Survey on sentiment analysis: evolution of research methods and topics

Area of Science:

• Natural Language Processing
• Computational Linguistics
• Data Science

Background:

• Sentiment analysis is a rapidly growing research area within Natural Language Processing.
• Existing literature reviews often focus on techniques or applications, lacking a historical perspective on research evolution.
• Few surveys utilize keyword co-occurrence analysis for a comprehensive overview of sentiment analysis.

Purpose of the Study:

• To provide a comprehensive survey of sentiment analysis research, focusing on the evolution of research methods and topics over the past two decades.
• To analyze the interconnections between sentiment analysis methods and research themes.
• To identify emerging hotspots and trends in the field to guide future research.

Main Methods:

• Keyword co-occurrence analysis was employed to map the landscape of sentiment analysis research.
• A community detection algorithm was integrated with keyword analysis to identify research clusters and their evolution.
• The study analyzed research trends and topic evolution over a twenty-year period.

Main Results:

• The study maps the evolution of sentiment analysis research methods and topics over two decades.
• It identifies key research clusters and their temporal development, highlighting shifts in focus.
• Connections between specific methods and prevalent research topics were analyzed, revealing patterns.

Conclusions:

• This research offers a unique historical perspective on sentiment analysis, distinct from typical method-focused surveys.
• The findings provide valuable insights into the dynamic evolution of sentiment analysis research, guiding researchers toward emerging areas.
• The study identifies limitations and suggests future research directions within the sentiment analysis domain.