Extracting Linguistic Features From Opinion Data Streams For Multi-Domain Sentiment Analysis

The approach described in this paper explores the use of semantic structured representation of sentences extracted from texts for multi-domain sentiment analysis purposes. The presented algorithm is built upon a domain-based supervised approach using index-like structured for representing information extracted from text. The algorithm extracts dependency parse relationships from the sentences containing in a training set. Then, such relationships are aggregated in a semantic structured together with either polarity and domain information. Such information is exploited in order to have a more fine-grained representation of the learned sentiment information. When the polarity of a new text has to be computed, such a text is converted in the same semantic representation that is used (i) for detecting the domain to which the text belongs to, and then (ii), once the domain is assigned to the text, the polarity is extracted from the index-like structure. First experiments performed by using the Blitzer dataset for training the system demonstrated the feasibility of the proposed approach