Existing research on sentiment analysis mainly uses sentiment words and phrases to determine sentiments expressed in documents and sentences. Techniques have also been developed to find such words and phrases using dictionaries and domain corpora. However, there are still other types of words and phrases that do not bear sentiments on their own, but when they appear in some particular contexts, they imply positive or negative opinions. One class of such words or phrases is those that express resources such as water, electricity, gas, etc. For example, "this washer uses a lot of electricity" is negative but "this washer uses little water" is positive. Extracting such resource words and phrases are important for sentiment analysis. This paper formulates the problem based on a bipartite graph and proposes a novel iterative algorithm to solve the problem. Experimental results using diverse real-life sentiment corpora show good results.
REF argue that there are many types of expressions that do not bear sentiments on their own, but they imply sentiment in specific contexts.
Extracting Resource Terms for Sentiment Analysis
IJCNLP
Computer Science