Latent Semantic Analysis using a Dennis Coefficient for English Sentiment Classification in a Parallel System

Abstract

Abstract:We have already survey many significant approaches for many years because there are many crucial contributions of the sentiment classification which can be applied in everyday life, such as in political activities, commodity production, and commercial activities. We have proposed a novel model using a Latent Semantic Analysis (LSA) and a Dennis Coefficient (DNC) for big data sentiment classification in English. Many LSA vectors (LSAV) have successfully been reformed by using the DNC. We use the DNC and the LSAVs to classify 11,000,000 documents of our testing data set to 5,000,000 documents of our training data set in English. This novel model uses many sentiment lexicons of our basis English sentiment dictionary (bESD). We have tested the proposed model in both a sequential environment and a distributed network system. The results of the sequential system are not as good as that of the parallel environment. We have achieved 88.76% accuracy of the testing data set, and this is better than the accuracies of many previous models of the semantic analysis. Besides, we have also compared the novel model with the previous models, and the experiments and the results of our proposed model are better than that of the previous model. Many different fields can widely use the results of the novel model in many commercial applications and surveys of the sentiment classification.

An efficient framework of utilizing the latent semantic analysis in text extraction

“…We showed that the use of terms overlaps and the traditional statistics based on tf-idf analysis in an individual manner (without semantic investigation) failed to remove a reasonable portion of the text (CR = 68%, only 32% from the text is removed). And in [17,19,20,21,22,29,45], the authors showed that the semantic analysis of the text using a powerful semantic analyzer such the LSA increases the accuracy but the time complexity was the challenge. Therefore, the proposed MLS method draws a map on which kind of text analysis should be used for each part of the text.…”

“…Other statistical techniques used to extract semantically related sentences in a document are the LSA [17,19,20,21,29,45] and the Neural Network [12,23,24,25,56]. Different Neural Network (NN) methods have been used to extract summaries from the text document.…”

“…The Statistical approaches give the terms and the sentences quantitative scores based on the presence of certain features. The statistical approaches that are used in text summarization include: Vector Space Model [13 -15], Machine Learning [16], Fuzzy Logic [17], Latent Semantic Analysis [17,19,20,21,22,29,45], and Neural Networks [23 -25]. The Linguistic approaches are language dependent, and they extract the summary based on the linguistic features or structures (see [26 -28]).…”

“…In this paper, we are interested in the statistical approaches especially the Latent Semantic Analysis. LSA can simulate the way people acquire knowledge and meaning through the correlation of facts from several sources [29]. LSA goes beyond the traditional statistical approaches and approximates the semantic meaning of the sentence through the exploration of the meaning of each word found in that sentence or document.…”

“…The LSA assumes that the text meaning is the collection of the meanings of the words found in the text and those meanings should be captured by analyzing huge text corpus. In the literature, researchers showed that the LSA is a significant tool in text mining because it addresses the semantic meaning that solves some of the problems of the other statistical approaches [17,19,20,21,22,29,45]. As stated in [30] the LSA was proposed in the field of Information Retrieval and Natural Language Processing (NLP) to solve two main problems in the VSM model, the synonyms and polysemy: (1) Synonyms problem: it arises when two or more different terms have the same meaning.…”