Sentimental Analysis on Twitter Data using Naive Bayes

Abstract

Sentiment Analysis (SA) and summarization has
recently become the focus of many researchers, because analysis
of online text is beneficial and demanded in many different applications. One such application is product-based sentiment summarization of multi-documents with the purpose of informing users
about pros and cons of various products. This paper introduces
a novel solution to target-oriented sentiment summarization and
SA of short informal texts with a main focus on Twitter posts
known as tweets. We compare different algorithms and methods
for SA polarity detection and sentiment summarization. We show
that our hybrid polarity detection system not only outperforms
the unigram state-of-the-art baseline, but also could be an
advantage over other methods when used as a part of a sentiment
summarization system. Additionally, we illustrate that our SA
and summarization system exhibits a high performance with
various useful functionalities and features.Sentiment classication
aims to automatically predict sentiment polarity (e.g., positive or
negative) of users publishing sentiment data (e.g., reviews, blogs).
Although traditional classication algorithms can be used to train
sentiment classiers from manually labeled text data, the labeling
work can be time-consuming and ex-pensive. Meanwhile, users
often use some different words when they express sentiment in
different domains. If we directly apply a classier trained in one
domain to other domains, the performance will be very low due to
the differences between these domains. In this work, we develop
a general solution to sentiment classication when we do not have
any labels in a target domain but have some labeled data in a
different domain, regarded as source domain.