Aspect Based Sentiment Analysis to Extract Meticulous Opinion Value
This work addresses the need for meticulous opinion extraction in educational feedback, but it appears incremental as it applies existing dictionary-based methods to a specific domain without major innovations.
The paper tackles the problem of extracting detailed sentiment from teacher remarks by proposing an algorithm for aspect-level sentiment analysis, which calculates aspect values combined with opinion and sentiment values to produce a summarized remark score.
Opinion Mining and Sentiment Analysis is a process of identifying opinions in large unstructured/structured data and then analysing polarity of those opinions. Opinion mining and sentiment analysis have found vast application in analysing online ratings, analysing product based reviews, e-governance, and managing hostile content over the internet. This paper proposes an algorithm to implement aspect level sentiment analysis. The algorithm takes input from the remarks submitted by various teachers of a student. An aspect tree is formed which has various levels and weights are assigned to each branch to identify level of aspect. Aspect value is calculated by the algorithm by means of the proposed aspect tree. Dictionary based method is implemented to evaluate the polarity of the remark. The algorithm returns the aspect value clubbed with opinion value and sentiment value which helps in concluding the summarized value of remark.