Abstract:
The amount of unstructured data on the internet is increasing with each passing day. People express
their opinions on social media, blogs, and forums in different ways. Therefore, the need to process
that unstructured data grows as well. The processing is important for extracting knowledge patterns
from the data available online. Sentiment analysis is a popular technique that is used for knowledge
extraction from people’s opinions. There are many businesses and product developers that use
people’s opinions as a basis for improvement. We have seen great progress in Natural language
processing and Sentiment Analysis techniques over the past few years. However, traditional
sentiment analysis approaches focused on some particular type of data by using machines learning
models and failed to achieve the best performance in terms of accuracy. There are many short
comings in previous studies. Therefore, a major challenge is to overcome these problems to extract
useful data from the huge amount of data that is available online. This thesis has been conducted
to reduce the research gap by coming up with a better solution to improve the accuracy of the
existing models used for sentence level sentiment analysis tasks. Thus, we have proposed a neural
network-based sequence model (RNN-LSTM) that is used for the sentiment classification from
opinionated sentences. Our Sentiment classification model is based on two state of the art deep
learning algorithms Recurrent Neural Network (RNN) and (LSTM). We have evaluated our
approach on four sentiment classification datasets. Furthermore, we have also made a detailed
comparison with popular baseline approaches. The results prove that the proposed technique
acheives improved accuracy as compared to the existing models.
