Pablo of adjectives. Xia et al. [13] used an

Pablo et. al. 9 presented variations of Naive Bayes
classifiers for detecting polarity of English tweets. Two different variants of
Naive Bayes classifiers were built namely Baseline (trained to classify tweets
as positive, negative and neutral), and Binary (makes use of a polarity lexicon
and classifies as positive and negative. Neutral tweets neglected). The
features considered by classifiers were Lemmas (nouns, verbs, adjectives and
adverbs), Polarity Lexicons, and Multiword from different sources and Valence
Shifters.

Turney et al 11 used bag-of-words method for
sentiment analysis in which the relationships between words was not at all
considered and a document is represented as just a collection of words. To
determine the sentiment for the whole document, sentiments of every word was
determined and those values are united with some aggregation functions.

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Kamps et al. 12 used the lexical database WordNet to
determine the emotional content of a word along different dimensions. They
developed a distance metric on WordNet and determined semantic polarity of
adjectives.

Xia et al. 13 used an ensemble framework for
Sentiment Classification which is obtained by combining various feature sets
and classification techniques. In their work, they used two types of feature
sets (Part-of-speech information and Word relations) and three base classifiers
(Naive Bayes, Maximum Entropy and Support Vector Machines) . They applied
ensemble approaches like fixed combination, weighted combination and
Meta-classifier combination for sentiment classification and obtained better
accuracy.

Alexander T et al.14 have proposed a model to tackle
the problem of sentence level sentiment classification. They have classified
the text into three classes i.e. positive, negative and neutral. The TTS
framework is built without using the additional textual data. Till this
invention, no attempt was done to use SA methods for TTS requirements. The
classifiers are trained to classify the sentiments rely on the representation
of the features.

Shulong Tan et al.15Twitter sentiment analysis is an
important research area for academic as well as business fields for decision
making like for the seller to decide if the product should be produced in a
large quantity as per the buyers feedback and for the students to decide if the
study material to be referred or not. in this work, Shulong Tan et al. have
proposed LDA based two models to interpret the sentiment variations on twitter
i.e.-LDA to distill out the foreground topics and RCB-LDA to find out the
reasons why public sentiments have been changed for the target.

CONCLUSION

In this paper, we provide a survey and comparative
study of existing techniques for opinion mining including machine learning and
lexicon-based approaches, together with cross domain and cross-lingual methods
and some evaluation metrics. Research results show that machine learning
methods, such as SVM and naive Bayes have the highest accuracy and can be
regarded as the baseline learning methods, while lexicon-based methods are very
effective in some cases, which require few efforts in human-labeled document
.We also studied the effects of various features on classifier. We can conclude
that more the cleaner data, more accurate results can be obtained. Use of
bigram model provides better sentiment accuracy as compared to other models. We
can focus on the study of combining machine learning method into opinion
lexicon method in order to improve the accuracy of sentiment classification and
adaptive capacity to variety of domains.

References