Abstract:
Sentimental analysis is a technique which is used to classify different types of
documents as positive, negative or neutral. Hand written form, mails, telephone
surveys or online feedback forms are used to get customer feedbacks about products
and services. In fact, sentimental analysis is the technique which is used to mine
online and offline customer feedback data to gain insight of product and services
automatically. Since business types are different, it is quite challenging to develop a
generic sentimental analyzer. Therefore, this ongoing research focused on
developing a generic framework that can be extended further in future to develop the
best generic sentiment analyzer. Several online customer feedback forms were used
as the dataset. Webpage scraping module was developed to extract the reviews from
web pages and chunk and chink rules were developed to extract the comparative and
superlative adverbs to build the knowledge base. The web site (Thesaurus.com) was
used to build the test data with synonyms of good, bad and neutrals. Next WordNet
database was used with different semantic similarity algorithms such as path
similarity, Leacock-Chodorow-similarity, Wu- Palmer-Similarity and Jiang-conrath
similarity to test the sentiments. Accuracy of this framework was improved further
with the vector model built with natural language processing techniques. Label
dataset of amazon product reviews provided by University of Pennsylvania were
used to test the accuracy. Framework was developed to change the multiplied value
based on the domain. The accuracy of the final sentiment value was given as a
percentage of the positive or negative type. This framework gave fairly accurate
results which are useful to generate good insights with user reviews.
