Lexicon‐pointed hybrid N‐gram Features Extraction Model (LeNFEM) for sentence level sentiment analysis

Abstract

Sentiment analysis of social media textual posts can provide information and knowledge that is applicable in social settings, business intelligence, evaluation of citizens' opinions in governance, and in mood triggered devices in the Internet of Things. Feature extraction and selection is a key determinant of accuracy and computational cost of machine learning models for such analysis. Most feature extraction and selection techniques utilize bag of words, N‐grams, and frequency‐based algorithms especially Term Frequency‐Inverse Document Frequency. However, these approaches do not consider relationships between words, they ignore words' characteristics and they suffer high feature dimensionality. In this paper we propose and evaluate a feature extraction and selection approach that utilizes a fixed hybrid N‐gram window for feature extraction and minimum redundancy maximum relevance feature selection algorithm for sentence level sentiment analysis. The approach improves the existing features extraction techniques, specifically the N‐gram by generating a hybrid vector from words, Part of Speech (POS) tags, and word semantic orientation. The vector is extracted by using a static trigram window identified by a lexicon where a sentiment word appears in a sentence. A blend of the words, POS tags, and the sentiment orientations of the static trigram are used to build the feature vector. The optimal features from the vector are then selected using minimum redundancy maximum relevance (MRMR) algorithm. Experiments were carried out using the public Yelp dataset to compare the performance of the proposed model and existing feature extraction models (BOW, normal N‐grams and lexicon‐based bag of words semantic orientations). Using supervised machine learning classifiers the experimental results showed that the proposed model had the highest F‐measure (88.64%) compared to the highest (83.55%) from baseline approaches. Wilcoxon test carried out ascertained that the proposed approach performed significantly better than the baseline approaches. Comparative performance analysis with other datasets further affirmed that the proposed approach is generalizable.

Sentiment Analysis of Text Reviews Using Lexicon-Enhanced Bert Embedding (LeBERT) Model with Convolutional Neural Network

“…This can be guided by utilization of sentiment lexicon and word N-grams. In a recent study [13], the researchers investigated a text representation technique using sentiment lexicon and N-grams where a Lexicon-pointed hybrid N-gram feature extraction model (LeNFEM) was proposed and investigated. A three-word N-gram was identified, which contains a sentiment word by use of a sentiment lexicon.…”

“…(2) deep learning-based automated vector representation approaches such as word embeddings [11][12][13]. Word Embedding is one of the most useful deep learning methods used for constructing vector representations of words and documents in text classification tasks.…”

Graph embedding approach to analyze sentiments on cryptocurrency

“…For example, if a business launches a new product, sentiment analysis can help them track how customers are reacting to it, and if they are satisfied or not. NLP techniques such as text preprocessing [7], feature extraction [8], and classification [9] have been widely used in sentiment analysis to identify sentiment polarity (positive, negative, or neutral) of text. Machine learning algorithms [10], including logistic regression [11], naive Bayes [12], and support vector machines (SVM) [13], have been widely used in sentiment analysis to classify text into sentiment categories.…”

Sentiment Analysis on Text Reviews Using Lexicon Selected-Bert Embedding (LeBERT) Model with Convolutional Neural Network

“…This can be guided by utilization of sentiment lexicon and word N-grams. In a recent study [13], the researchers investigated a text representation technique using sentiment lexicon and N-grams where a Lexicon-pointed hybrid N-gram Feature Extraction Model (LeNFEM) was proposed and investigated. A three word N-gram was identi ed that contains a sentiment word by use of a sentiment lexicon.…”