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Classifying product reviews is one of the tasks in Natural Language Processing by which the sentiment of the reviewer towards a product can be identified. This identification is useful for the growth of the business by increasing the number of satisfied customers through product quality improvement. Bigram models are more popular in performing this classification since it considers the occurrence of two words consecutively in the reviews. In the existing works on bigram models, semantically similar words to the words present in bigrams are not considered. As the reviewers use different words with the same meaning to express their feeling, we proposed improved bigram models in which semantically similar words to the words in bigrams are also used for classifying the reviews. In the proposed models, sentiment polarity thesaurus is constructed by including sentiment words and their synonyms. The combinations of constructed thesaurus, Synset and Word2Vec are used for extracting synonyms for the words in the reviews. Performance of the proposed models is compared with the traditional bigram model and state-of-the-art methods. It is observed from the results that our models are able to achieve better performance than traditional model and recent methods.