Sentiment analysis has become an important area of research in natural language processing. This technique has a wide range of applications, such as comprehending user preferences in ecommerce feedback portals, politics, and in governance. However, accurate sentiment analysis requires robust text representation techniques that can convert words into precise vectors that represent the input text. There are two categories of text representation techniques: lexicon-based techniques and machine learning-based techniques. From research, both techniques have limitations. For instance, pre-trained word embeddings, such as Word2Vec, Glove, and bidirectional encoder representations from transformers (BERT), generate vectors by considering word distances, similarities, and occurrences ignoring other aspects such as word sentiment orientation. Aiming at such limitations, this paper presents a sentiment classification model (named LeBERT) combining sentiment lexicon, N-grams, BERT, and CNN. In the model, sentiment lexicon, N-grams, and BERT are used to vectorize words selected from a section of the input text. CNN is used as the deep neural network classifier for feature mapping and giving the output sentiment class. The proposed model is evaluated on three public datasets, namely, Amazon products’ reviews, Imbd movies’ reviews, and Yelp restaurants’ reviews datasets. Accuracy, precision, and F-measure are used as the model performance metrics. The experimental results indicate that the proposed LeBERT model outperforms the existing state-of-the-art models, with a F-measure score of 88.73% in binary sentiment classification.

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 social media posts and texts can provide information and knowledge that is applicable in social settings, business intelligence, evaluation of citizens’ opinions in governance and mood triggered devices in 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 such as N-grams and frequency-based algorithms especially Term Frequency-Inverse document frequency (TF-IDF). However, these approaches suffer shortcomings such as; they do not consider relationships between words, they ignore words’ characteristics and they suffer high feature dimensionality. In this paper we propose and evaluate an approach that utilizes a fixed hybrid N-gram window for feature extraction and Minimum Redundancy Maximum Relevance feature selection for sentence level sentiment analysis. The approach improves the existing feature extraction techniques specifically the N-gram by generating a tri-gram vector from words, Part of speech tags and word semantic orientation. The N-gram vector is extracted by employing a static 3-gram 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 3N-gram are used to build the feature vector. The optimal features from the vector are then selected using Minimum Redundancy Maximum Relevance (MR2) algorithm. Experiments were carried out with a publicly available yelp tweets dataset to evaluate the performance of four supervised machine learning classifiers (Naïve Bayes, K-Nearest Neighbor, Decision Tree and Support Vector Machines) when augmented with the proposed model. The results showed that the proposed model had the highest accuracy (86.85%), recall (86.85%) and precision (86.96%).

Sentiment analysis has become an important area of research in natural language processing. This technique has a wide range of applications such as comprehending user preferences in ecommerce feedback portals, politics, and in governance. However, accurate sentiment analysis requires robust text representation techniques that can convert words into precise vectors that represent the input text. There are two categories of text representation techniques: lexicon-based techniques and machine learning-based techniques. From research, both techniques have limitations. For instance, pre-trained word embeddings such as Word2Vec, Glove and Bidirectional Encoder Representations from Transformers (BERT) generate vectors by considering word distances, similarities and occurrences ignoring other aspects such as word sentiment orientation. Aiming at such limitations, this paper presents a sentiment classification model (named LeBERT) combining Sentiment Lexicon, N-grams, BERT and CNN. In the model, Sentiment Lexicon, N-grams and BERT are used to vectorize words selected from a section of the input text. CNN is used as the deep neural network classifier for feature mapping and giving the output sentiment class. The proposed model is evaluated on Yelp’s three datasets (movie, restaurant and products’ reviews) using accuracy, precision and F-measure as performance metrics. The experimental results indicate that the proposed LeBERT model outperform the existing state-of-the-art models with an F-measure score of 88.73% in binary sentiment classification.