Sentiment analysis of the text deals with the mining of the opinions of people from their written communication. With the increasing usage of online social media platforms for user interactions, abundant opinionated textual data emerges. Therefore, it leads to increased mining of opinions and sentiments and hence greater interest in sentiment analysis. The article introduces the novel Lexico-Semantic features and their use in the sentiment polarity task of English language text. These features are derived using the semantic extension of the lexicons by employing sentiment lexicons and semantic models. These features make data sample size consistent when used in deep learning settings, thereby eliminating the zero padding. For evaluation, we use different semantic models and lexicons to determine the role and impact of Lexico-Semantic features in classification performance. These features, along with the other features, are used to train the different classifiers. Our experimental evaluation shows that introducing Lexico-Semantic features to various state-of-the-art methods of both machine and deep learning improves the overall performance of classifiers.