| dc.description.abstract | It has become a common practice on the web for a consumer to learn how others like or
dislike a product before buying, or for a manufacturer or service provider to keep track of
customer opinions on its products so as to improve the user satisfaction. However, as the
number of reviews available for any given product or service grows, it becomes harder and
more difficult for people to understand and evaluate what the majority opinions about the
product are.
In this work, we attempt to resolve this problem for customer care service center in a Kenyan
context by applying Sentiment analysis on people’s opinions expressed on social media.
Sentiment Analysis or opinion mining attempts to resolve this problem by first presenting the
user with an aggregate view of the entire data set, summarized by a label or a score, and
secondly by segmenting the opinions/sentiments into three classes (positive, negative and
neutral) that can be further explored as desired.
We began by developing a module for facilitating searching, extraction and storage of
opinions from social media. This was followed by development and training of a polarity
classifier using the Python Natural Language Toolkit (NLTK) where we used the Naïve
Bayes machine learning technique. The study went ahead to develop a web based application
that integrates Facebook Graph API, Twitter API and the developed classifier to provide
functionality for extracting, classifying and presenting classification results of data obtained
from social media in a manner that can give a user with summarized as well as detailed view
of customer opinion about a service or product. | en |
