Sentiment Polarity Of Words Research Articles

Hybrid Feature Extraction for Multi-Label Emotion Classification in English Text Messages

Emotions are vital for identifying an individual’s attitude and mental condition. Detecting and classifying emotions in Natural Language Processing applications can improve Human–Computer Interaction systems, leading to effective decision making in organizations. Several studies on emotion classification have employed word embedding as a feature extraction method, but they do not consider the sentiment polarity of words. Moreover, relying exclusively on deep learning models to extract linguistic features may result in misclassifications due to the small training dataset. In this paper, we present a hybrid feature extraction model using human-engineered features combined with deep learning based features for emotion classification in English text. The proposed model uses data augmentation, captures contextual information, integrates knowledge from lexical resources, and employs deep learning models, including Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Encoder Representation and Transformer (BERT), to address the issues mentioned above. The proposed model with hybrid features attained the highest Jaccard accuracy on two of the benchmark datasets, with 68.40% on SemEval-2018 and 53.45% on the GoEmotions dataset. The results show the significance of the proposed technique, and we can conclude that the incorporation of the hybrid features improves the performance of the baseline models.

Sentiment analysis of weather news in British online newspapers

The advancement of modern technologies has influenced the way news is presented and consumed, particularly online. Weather is an important topic for the public as it relates to the human experience and addresses current societal issues. In this paper, we introduce a systematic approach to conduct sentiment analysis of weather news stories, to specify the emotional tone and examine the role of subjectivity in online news reporting. This research falls within the scope of a lexicon-based (unsupervised) approach to sentiment analysis, which involves finding the sentiment polarity of words. The analysis is predominantly based on sentence-level sentiment analysis. Two popular online web services, MonkeyLearn and SentiStrength, were applied to automatically detect human emotions. We compared the efficiency of each tool and found that MonkeyLearn provided better final results in comparison to SentiStrength, which tended to misclassify negative sentiments into neutral ones. The final results of frequency calculation showed the dominance of weather news stories with negative sentiment polarity over positive and neutral ones, with neutral sentiments being in the minority. Based on the empirical findings, we observed an objectivity-to-subjectivity shift in online news reporting.

Khmer Sentiment Lexicon Based on PU Learning and Label Propagation Algorithm

The sentiment lexicon is an important tool for natural language processing tasks. In addition to being able to determine the sentiment polarity of words or phrases, it can assist attribute-level, sentence-level, and text-level sentiment analysis tasks. In light of the fact that tagging data and corpora for the Khmer language are scarce, where most resources related to sentiment lexicons are for English, this paper proposes a method for constructing a sentiment lexicon for Khmer based on Positive-Unlabeled learning (PU Learning) and the label propagation algorithm. Sentiment words are first extracted from a corpus using the Spy technique of PU learning method. The main idea is to purify the set of N-class examples, train the MLP classifier, and continuously delete spy words and increase the number of P-class words in the iterative process. Following this, the sentiment polarity of the candidate words is determined. By considering the problem of determining the sentiment polarity of the candidate words as one of calculating its probability distribution, a small number of labeled sentiment words and candidate words are used to construct a graph model. The contextual information of the candidate words is used to construct a simple supplementary graph model of the set of sentiment words through word co-occurrence and triangulation, where this enhances the correlation between data items. The sentiment polarity of the candidate words is then determined through the label propagation algorithm. The results of experiments show that the proposed method can be used to construct a Khmer sentiment lexicon with a small number of labeled data and a small corpus without requiring excessive manual labeling.

Aspect-Level Sentiment Analysis Using CNN Over BERT-GCN

The increase in the volume of user-generated content on Twitter has resulted in tweet sentiment analysis becoming an essential tool for the extraction of information about Twitter users’ emotional state. Consequently, there has been a rapid growth of tweet sentiment analysis in the area of natural language processing. Tweet sentiment analysis is increasingly applied in many areas, such as decision support systems and recommendation systems. Therefore, improving the accuracy of tweet sentiment analysis has become practical and an area of interest for many researchers. Many approaches have tried to improve the performance of tweet sentiment analysis methods by using the feature ensemble method. However, most of the previous methods attempted to model the syntactic information of words without considering the sentiment context of these words. Besides, the positioning of words and the impact of phrases containing fuzzy sentiment have not been mentioned in many studies. This study proposed a new approach based on a feature ensemble model related to tweets containing fuzzy sentiment by taking into account elements such as lexical, word-type, semantic, position, and sentiment polarity of words. The proposed method has been experimented on with real data, and the result proves effective in improving the performance of tweet sentiment analysis in terms of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score.

Neural Networks with Emotion Associations, Topic Modeling and Supervised Term Weighting for Sentiment Analysis.

Automated sentiment analysis is becoming increasingly recognized due to the growing importance of social media and e-commerce platform review websites. Deep neural networks outperform traditional lexicon-based and machine learning methods by effectively exploiting contextual word embeddings to generate dense document representation. However, this representation model is not fully adequate to capture topical semantics and the sentiment polarity of words. To overcome these problems, a novel sentiment analysis model is proposed that utilizes richer document representations of word-emotion associations and topic models, which is the main computational novelty of this study. The sentiment analysis model integrates word embeddings with lexicon-based sentiment and emotion indicators, including negations and emoticons, and to further improve its performance, a topic modeling component is utilized together with a bag-of-words model based on a supervised term weighting scheme. The effectiveness of the proposed model is evaluated using large datasets of Amazon product reviews and hotel reviews. Experimental results prove that the proposed document representation is valid for the sentiment analysis of product and hotel reviews, irrespective of their class imbalance. The results also show that the proposed model improves on existing machine learning methods.

Interactive POS-aware network for aspect-level sentiment classification

Existing aspect-level sentiment-classification models completely rely on the learning from given datasets. However, these are easily misled by biased samples, resulting in learning some ill-suited rules that limit their potential. The information of some specific part-of-speech (POS) categories often indicates the word sentiment polarity, which can be introduced as prior knowledge to facilitate prediction of the model. Accordingly, we propose an interactive POS-aware network (IPAN) that explicitly introduces the POS information as reliable guidance to assist the model in accurately predicting sentiment polarity. We distinguish the information of different POS categories using a POS-filter gate and reinforce the features extracted from adjectives, adverbs, and verbs via a POS-highlighting attention mechanism. This enables the model to concentrate on the words that contain significant sentiment orientations and to obtain the most practical learning experience. To emphasize the target information, we construct a target-context gate that enables the interaction of the target information with contexts; consequently, the model considerably focuses on target-related sentiment features. The experiments on SemEval2014 and Twitter datasets verify that our IPAN consistently outperforms the current state-of-the-art methods.

Improving the Performance of Sentiment Analysis of Tweets Containing Fuzzy Sentiment Using the Feature Ensemble Model

The increase in the volume of user-generated content on Twitter has resulted in tweet sentiment analysis becoming an essential tool for the extraction of information about Twitter users' emotional state. Consequently, there has been a rapid growth of tweet sentiment analysis in the area of natural language processing. Tweet sentiment analysis is increasingly applied in many areas, such as decision support systems and recommendation systems. Therefore, improving the accuracy of tweet sentiment analysis has become practical and an area of interest for many researchers. Many approaches have tried to improve the performance of tweet sentiment analysis methods by using the feature ensemble method. However, most of the previous methods attempted to model the syntactic information of words without considering the sentiment context of these words. Besides, the positioning of words and the impact of phrases containing fuzzy sentiment have not been mentioned in many studies. This study proposed a new approach based on a feature ensemble model related to tweets containing fuzzy sentiment by taking into account elements such as lexical, word-type, semantic, position, and sentiment polarity of words. The proposed method has been experimented on with real data, and the result proves effective in improving the performance of tweet sentiment analysis in terms of the $F_{1}$ score.

Sparse Self-Attention LSTM for Sentiment Lexicon Construction

Sentiment lexicon is a very important resource for opinion mining. Recently, many state-of-the-art works employ deep learning techniques to construct sentiment lexicons. In general, they firstly learn sentiment-aware word embeddings, and then use it as word features to construct sentiment lexicons. However, these methods do not consider the importance of each word to the distinguish of documents’ sentiment polarities. As we know, most words among a document do not contribute to understand documents’ semantic or sentiment. For example, in the tweet It's a good day, but i can't feel it. I'm really unhappy. The words ‘unhappy’, ‘feel’ and ‘can't’ are much more important than the words ‘good’, ‘day’ in predicting the sentiment polarity of this twitter. Meanwhile, many words, such as ‘the’, ‘in’, ‘it’ and ‘I'm’ are uninformative. In this paper, we propose a novel sparse self-attention LSTM SSALSTM to efficiently capture the above intuitive facts, and then construct a large scale sentiment lexicons in twitter. In SSALSTM, we use a novel self-attention mechanism to capture the importance of each words to the distinguish of documents’ sentiment polarities. In addition, a $L_1$ regularize is applied in the attentions which can ensure the sparsity characters that most words in a document are semantic and sentiment indistinguishable. Once we learn an efficient sentiment-aware word embedding, we train a classifier which uses sentiment-aware word embedding as features to predict the sentiment polarities of words. Extensive experiments on four publicly available datasets, SemEval 2013–2016, indicate that the sentiment lexicon generated by our proposed model achieves state-of-the-art performance on both supervised and unsupervised sentiment classification tasks.

Sentiment Lexicon Construction With Hierarchical Supervision Topic Model

In this paper, we propose a novel hierarchical supervision topic model to construct a topic-adaptive sentiment lexicon (TaSL) for higher-level classification tasks. It is widely recognized that sentiment lexicon as a useful prior knowledge is crucial in sentiment analysis or opinion mining. However, many existing sentiment lexicons are constructed ignoring the variability of the sentiment polarities of words in different topics or domains. For example, the word “amazing” can refer to causing great surprise or wonder but can also refer to very impressive and excellent. In TaSL, we solve this issue by jointly considering the topics and sentiments of words. Documents are represented by multiple pairs of topics and sentiments, where each pair is characterized by a multinomial distribution over words. Meanwhile, this generating process is supervised under hierarchical supervision information of documents and words. The main advantage of TaSL is that the sentiment polarity of each word in different topics can be sufficiently captured. This model is beneficial to construct a domain-specific sentiment lexicon and then effectively improve the performance of sentiment classification. Extensive experimental results on four publicly available datasets, MR , OMD , semEval13A , and semEval16B were presented to demonstrate the usefulness of the proposed approach. The results have shown that TaSL performs better than the existing manual sentiment lexicon (MPQA), the topic model based domain-specific lexicon (ssLDA), the expanded lexicons(Weka-ED, Weka-STS, NRC, Liu's), and deep neural network based lexicons (nnLexicon, HIT, HSSWE).

Sentiment Analysis of Mobile datasets using Naïve Bayes Algorithm

Sentiment Analysis is one of the pursued field of Natural Language Processing (NLP). It is an intellectual process of extracting user’s feelings and emotions. The evolution of Internet has driven massive amount of personalized reviews for various related information on the Web specially twitter. These reviews are beneficial for business persons for understanding customer interest, taking better decisions and planning processes. User sentiment refers to the emotions expressed by them through the text reviews. These sentiments can be positive, negative or neutral. The study explores user sentiments and expresses them in terms of user sentiment polarity. Sentiment Analysis poses as a powerful tool for users to extract the needful information, as well as to aggregate the collective sentiments of the reviews. In this paper we present, a lexicon-based approach for sentiment analysis on Twitter. We have used Naive Bayes algorithm to find sentiment polarities of words in tweeter datasets of some mobile brands. Our approach allows for the detection of sentiment at tweet-level. We evaluate our approach on various mobile brands datasets resulting into accuracy for sentiment polarity classification. We compare various parameters precision, F measure, recall help to improve accuracy.

Deep Convolution Neural Networks for Twitter Sentiment Analysis

Twitter sentiment analysis technology provides the methods to survey public emotion about the events or products related to them. Most of the current researches are focusing on obtaining sentiment features by analyzing lexical and syntactic features. These features are expressed explicitly through sentiment words, emoticons, exclamation marks, and so on. In this paper, we introduce a word embeddings method obtained by unsupervised learning based on large twitter corpora, this method using latent contextual semantic relationships and co-occurrence statistical characteristics between words in tweets. These word embeddings are combined with n-grams features and word sentiment polarity score features to form a sentiment feature set of tweets. The feature set is integrated into a deep convolution neural network for training and predicting sentiment classification labels. We experimentally compare the performance of our model with the baseline model that is a word n-grams model on five Twitter data sets, the results indicate that our model performs better on the accuracy and F1-measure for twitter sentiment classification.

How Objective a Neutral Word Is? A Neutrosophic Approach for the Objectivity Degrees of Neutral Words

In the latest studies concerning the sentiment polarity of words, the authors mostly consider the positive and negative constructions, without paying too much attention to the neutral words, which can have, in fact, significant sentiment degrees. More precisely, not all the neutral words have zero positivity or negativity scores, some of them having quite important nonzero scores for these polarities. At this moment, in the literature, a word is considered neutral if its positive and negative scores are equal, which implies two possibilities: (1) zero positive and negative scores; (2) nonzero, but equal positive and negative scores. It is obvious that these cases represent two different categories of neutral words that must be treated separately by a sentiment analysis task. In this paper, we present a comprehensive study about the neutral words applied to English as is developed with the aid of SentiWordNet 3.0: the publicly available lexical resource for opinion mining. We designed our study in order to provide an accurate classification of the so-called “neutral words” described in terms of sentiment scores and using measures from neutrosophy theory. The intended scope is to fill the gap concerning the neutrality aspect by giving precise measurements for the words’ objectivity.

Disambiguating context-dependent polarity of words: An information retrieval approach

The paper introduces PolaritySim – a novel approach to disambiguating context-dependent sentiment polarity of words. The task of resolving the polarity of a given word instance as positive or negative is addressed as an information retrieval problem. At the pre-processing stage, a vector of context features is built for each word w based on all its occurrences in the positive polarity corpus (consumer reviews with high ratings) and another vector – on its contexts in the negative polarity corpus (reviews with low ratings). Lexico-syntactic context features are automatically generated from dependency parse graphs of the sentences containing the word. These two vectors are treated as “documents”, one with positive and one with negative polarity. To resolve the contextual polarity of a specific instance of the word w in a given sentence, its context feature vector is built in the same way, and is treated as the “query”. An information retrieval (IR) model is then applied to calculate the similarity of the “query” to each of the two “documents”, with the polarity of the best matching “document” attributed to the “query”. The method uses no prior polarity sentiment lexicons or purposefully annotated training datasets. The only external resource used is a readily available corpus of user-rated reviews. Evaluation on different domains shows more effective performance compared to state-of-the-art baselines, Support Vector Machines (SVM) and Multinomial Naive Bayes (MNB) classifiers, on three out of four datasets. PolaritySim, SVM and MNB were also evaluated with an out-of-domain training corpus. The results indicate that PolaritySim is more effective and robust when used with an out-of-domain corpus compared to SVM and MNB. We conclude that an IR based approach can be an effective and robust alternative to machine learning approaches for disambiguating word-level polarity using either within-domain, or out-of-domain training corpora.

An Unsupervised Cross-Lingual Topic Model Framework for Sentiment Classification

Sentiment classification aims to determine the sentiment polarity expressed in a text. In online customer reviews, the sentiment polarities of words are usually dependent on the corresponding aspects. For instance, in mobile phone reviews, we may expect the long battery time but not enjoy the long response time of the operating system. Therefore, it is necessary and appealing to consider aspects when conducting sentiment classification. Probabilistic topic models that jointly detect aspects and sentiments have gained much success recently. However, most of the existing models are designed to work well in a language with rich resources. Directly applying those models on poor-quality corpora often leads to poor results. Consequently, a potential solution is to use the cross-lingual topic model to improve the sentiment classification for a target language by leveraging data and knowledge from a source language. However, the existing cross-lingual topic models are not suitable for sentiment classification because sentiment factors are not considered therein. To solve these problems, we propose for the first time a novel cross-lingual topic model framework which can be easily combined with the state-of-the-art aspect/sentiment models. Extensive experiments in different domains and multiple languages demonstrate that our model can significantly improve the accuracy of sentiment classification in the target language.

転移学習を用いたSNSにおける感情分析の精度向上と炎上検知への応用

In recent years, along with the popularization of SNS, the incidents, which are called flaming, that the number of negative comments surges are on the increase. This becomes a problem for companies because flamings hurt companies' reputation. In order to minimalize the damage of reputation, we propose the method that detects flamings by estimating the sentiment polarities of SNS comments. Because of the unique SNS characteristics such as repetition of same comments, the polarities of words are sometimes wrongly estimated. To alleviate this problem, transfer learning is introduced. In this research, the sentiment polarities of words are trained in every domain. This will enable to extract the words that are domain-specific and dictate the polarity of comments. These words are occurred in retweets. Transfer learning is implemented to non-extracted words by averaging the occurrence probabilities in other domains. These processes keep the polarities of important words that dictate the polarity of comments and modify the wrongly estimated polarities of words. The experimental results show that the proposed method improves the performance of estimating the sentiment polarity of comments. Moreover, flamings can be detected without missing by monitoring time course of the number of negative comments.

SentiTurkNet: a Turkish polarity lexicon for sentiment analysis

Sentiment analysis aims to extract the sentiment polarity of given segment of text. Polarity resources that indicate the sentiment polarity of words are commonly used in different approaches. While English is the richest language in regard to having such resources, the majority of other languages, including Turkish, lack polarity resources. In this work we present the first comprehensive Turkish polarity resource, SentiTurkNet, where three polarity scores are assigned to each synset in the Turkish WordNet, indicating its positivity, negativity, and objectivity (neutrality) levels. Our method is general and applicable to other languages. Evaluation results for Turkish show that the polarity scores obtained through this method are more accurate compared to those obtained through direct translation (mapping) from SentiWordNet.

Contextual semantics for sentiment analysis of Twitter

Sentiment analysis on Twitter has attracted much attention recently due to its wide applications in both, commercial and public sectors. In this paper we present SentiCircles, a lexicon-based approach for sentiment analysis on Twitter. Different from typical lexicon-based approaches, which offer a fixed and static prior sentiment polarities of words regardless of their context, SentiCircles takes into account the co-occurrence patterns of words in different contexts in tweets to capture their semantics and update their pre-assigned strength and polarity in sentiment lexicons accordingly. Our approach allows for the detection of sentiment at both entity-level and tweet-level. We evaluate our proposed approach on three Twitter datasets using three different sentiment lexicons to derive word prior sentiments. Results show that our approach significantly outperforms the baselines in accuracy and F-measure for entity-level subjectivity (neutral vs. polar) and polarity (positive vs. negative) detections. For tweet-level sentiment detection, our approach performs better than the state-of-the-art SentiStrength by 4–5% in accuracy in two datasets, but falls marginally behind by 1% in F-measure in the third dataset.

A Random Walk–Based Model for Identifying Semantic Orientation

Automatically identifying the sentiment polarity of words is a very important task that has been used as the essential building block of many natural language processing systems such as text classification, text filtering, product review analysis, survey response analysis, and on-line discussion mining. We propose a method for identifying the sentiment polarity of words that applies a Markov random walk model to a large word relatedness graph, and produces a polarity estimate for any given word. The model can accurately and quickly assign a polarity sign and magnitude to any word. It can be used both in a semi-supervised setting where a training set of labeled words is used, and in a weakly supervised setting where only a handful of seed words is used to define the two polarity classes. The method is experimentally tested using a gold standard set of positive and negative words from the General Inquirer lexicon. We also show how our method can be used for three-way classification which identifies neutral words in addition to positive and negative words. Our experiments show that the proposed method outperforms the state-of-the-art methods in the semi-supervised setting and is comparable to the best reported values in the weakly supervised setting. In addition, the proposed method is faster and does not need a large corpus. We also present extensions of our methods for identifying the polarity of foreign words and out-of-vocabulary words.

Research of Caption Text Security Detection in Video Based on Semantic Orientation

With the rapid development of Internet technologies and applications, a lot of harmful video have spread out on Internet, it is enormously harmful to stability of social and people's physical and mental health. The means to extract video caption text is studied in this paper, and improvement method of text security detection. The proposed method first classifies caption text, then compares the result of classification with the library of user’s demands to determine whether to trigger alarms, through which the aim of monitoring harmful videos could be achieved. In this method, the text detection manner calculate the polarity of sentiment words by analyzing the context of those, meanwhile considers the effect of noun, then gets the orientation of the whole texts. Experiment has shown the method can monitor harmful video effectively.

Determine the Polarity of Domain-Specific Sentiment Words with Usage of Semantic Pattern of Sentences

Text sentiment analysis is a new branch of computational linguistics which is widely concerned. In this paper, we present an approach to determine polarity of sentiment word based on context of sentence. We first change the context of sentence to semantic pattern vector, calculate the between different sentences, then compare sentences context indirectly by comparing similarity of their pattern vector, next we annotate polarity of sentiment word according to comparing result. Experiment shows that when the context of two sentences have high similarity, it is likely to have high precision in recognizing polarity of sentiment word. Our study shows it's feasible to use semantic pattern vector in representing context and judging polarity of sentiment words.