Sentence Sentiment Research Articles

Evaluating criminal justice reform during COVID-19: The need for a novel sentiment analysis package.

The health and safety of incarcerated persons and correctional personnel have been prominent in the U.S. news media discourse during the COVID-19 pandemic. Examining changing attitudes toward the health of the incarcerated population is imperative to better assess the extent to which the general public favors criminal justice reform. However, existing natural language processing lexicons that underlie current sentiment analysis (SA) algorithms may not perform adequately on news articles related to criminal justice due to contextual complexities. News discourse during the pandemic has highlighted the need for a novel SA lexicon and algorithm (i.e., an SA package) tailored for examining public health policy in the context of the criminal justice system. We analyzed the performance of existing SA packages on a corpus of news articles at the intersection of COVID-19 and criminal justice collected from state-level outlets between January and May 2020. Our results demonstrated that sentence sentiment scores provided by three popular SA packages can differ considerably from manually-curated ratings. This dissimilarity was especially pronounced when the text was more polarized, whether negatively or positively. A randomly selected set of 1,000 manually scored sentences, and the corresponding binary document term matrices, were used to train two new sentiment prediction algorithms (i.e., linear regression and random forest regression) to verify the performance of the manually-curated ratings. By better accounting for the unique context in which incarceration-related terminologies are used in news media, both of our proposed models outperformed all existing SA packages considered for comparison. Our findings suggest that there is a need to develop a novel lexicon, and potentially an accompanying algorithm, for analysis of text related to public health within the criminal justice system, as well as criminal justice more broadly.

Lexicon-Based Sentiment Convolutional Neural Networks for Online Review Analysis

With the growing availability and popularity of sentiment-rich resources like blogs and online reviews, new opportunities and challenges have emerged regarding the identification, extraction, and organization of sentiments from user-generated documents or sentences. Recently, many studies have exploited lexicon-based methods or supervised learning algorithms to conduct sentiment analysis tasks separately; however, the former approaches ignore contextual information of sentences and the latter ones do not take sentiment information embedded in sentiment words into consideration. To tackle these limitations, we propose a new model named Sentiment Convolutional Neural Network (SentiCNN) to analyze the sentiments of sentences with both contextual and sentiment information of sentiment words, in which, contextual information is captured from word embeddings and sentiment information is identified using existing lexicons. We incorporate a Highway Network into our model to adaptively combine sentiment and contextual information from sentences by strengthening the connection between features of both sentences and their sentiment words. Furthermore, we propose three lexicon-based attention mechanisms (LBAMs) for our SentiCNN model to find the most important indicators of sentiments and make predictions more effectively. Experiments over two well-known datasets indicate that sentiment words, the Highway Network, and LBAMs contribute to sentiment analysis.

Open Vocabulary Electroencephalography-to-Text Decoding and Zero-Shot Sentiment Classification

State-of-the-art brain-to-text systems have achieved great success in decoding language directly from brain signals using neural networks. However, current approaches are limited to small closed vocabularies which are far from enough for natural communication. In addition, most of the high-performing approaches require data from invasive devices (e.g., ECoG). In this paper, we extend the problem to open vocabulary Electroencephalography(EEG)-To-Text Sequence-To-Sequence decoding and zero-shot sentence sentiment classification on natural reading tasks. We hypothesis that the human brain functions as a special text encoder and propose a novel framework leveraging pre-trained language models (e.g., BART). Our model achieves a 40.1% BLEU-1 score on EEG-To-Text decoding and a 55.6% F1 score on zero-shot EEG-based ternary sentiment classification, which significantly outperforms supervised baselines. Furthermore, we show that our proposed model can handle data from various subjects and sources, showing great potential for a high-performance open vocabulary brain-to-text system once sufficient data is available. The code is made publicly available for research purpose at https://github.com/MikeWangWZHL/EEG-To-Text.

Aspect Based Sentiment Analysis

Sentiment analysis, which addresses the computational treatment of opinion, sentiment, and subjectivity in text, has received considerable attention in recent years. In contrast to the traditional coarse-grained sentiment analysis tasks, such as document-level sentiment classification, we are interested in the fine-grained aspect-based sentiment analysis that aims to identify aspects that users comment on and these aspects’ polarities. Aspect-based sentiment analysis relies heavily on syntactic features. However, the reviews that this task focuses on are natural and spontaneous, thus posing a challenge to syntactic parsers. In this paper, we address this problem by proposing a framework of adding a sentiment sentence compression (Sent Comp) step before performing the aspect-based sentiment analysis. We apply a discriminative conditional random field model, with certain special features, to automatically compress sentiment sentences. Sentiment analysis is contextual mining of text which identifies and extracts subjective information in textual data. sentiment analysis proves to be an incredible asset for users to extract essential information and assists organizations with understanding the social sentiment of their brand, product or service while monitoring online conversations.

Analyzing and Visualizing Text Information in Corporate Sustainability Reports Using Natural Language Processing Methods

Sustainability is a major contemporary issue that affects everyone. Many companies now produce an annual sustainability report, mainly intended for their stakeholders and the public, enumerating their goals and degrees of achievement regarding sustainable development. Although sustainability reports are an important resource to understand a company’s sustainability strategies and practices, the difficulty of extracting key information from dozens or hundreds of pages with sustainability and business jargon has highlighted the need for metrics to effectively measure the content of such reports. Accordingly, many researchers have attempted to analyze the concepts and messages from sustainability reports using various natural language processing (NLP) methods. In this study, we propose a novel approach that overcomes the shortcomings of previous studies. Using the sentence similarity method and sentiment analysis, the study clearly shows thematic practices and trends, as well as a significant difference in the balance of positive and negative information in the reports across companies. The results of sentiment analysis prove that the new approach of this study is very useful. It confirms that companies actively use the sustainability report to improve their positive image when they experience a crisis. It confirms that companies actively use the sustainability report to improve their positive image when they experience a crisis. The inferences gained from this method will not only help companies produce better reports that can be utilized effectively, but also provide researchers with ideas for further research. In the concluding section, we summarize the implications of our approach and discuss limitations and future research areas.

Does attention mechanism possess the feature of human reading? A perspective of sentiment classification task

PurposeTo understand the meaning of a sentence, humans can focus on important words in the sentence, which reflects our eyes staying on each word in different gaze time or times. Thus, some studies utilize eye-tracking values to optimize the attention mechanism in deep learning models. But these studies lack to explain the rationality of this approach. Whether the attention mechanism possesses this feature of human reading needs to be explored.Design/methodology/approachThe authors conducted experiments on a sentiment classification task. Firstly, they obtained eye-tracking values from two open-source eye-tracking corpora to describe the feature of human reading. Then, the machine attention values of each sentence were learned from a sentiment classification model. Finally, a comparison was conducted to analyze machine attention values and eye-tracking values.FindingsThrough experiments, the authors found the attention mechanism can focus on important words, such as adjectives, adverbs and sentiment words, which are valuable for judging the sentiment of sentences on the sentiment classification task. It possesses the feature of human reading, focusing on important words in sentences when reading. Due to the insufficient learning of the attention mechanism, some words are wrongly focused. The eye-tracking values can help the attention mechanism correct this error and improve the model performance.Originality/valueOur research not only provides a reasonable explanation for the study of using eye-tracking values to optimize the attention mechanism but also provides new inspiration for the interpretability of attention mechanism.

Text highlighting methodology for consumer attitude measurement: A case study on vertical farming

Attitude measurement occupies a central position in consumer research. Concerns over the validity and reliability of traditional measures have motivated the development of alternative approaches. The present research introduces text highlighting as a method for attitude measurement using a case study on vertical farming (VF). A total of 837 UK consumers participated in an online survey, where they read a text about VF and used highlighting functions to mark text as ‘like’ and ‘dislike.’ Responses were systematic and logical: desirable aspects of VF were frequently highlighted as ‘like’, whereas undesirable aspects were more frequently highlighted as ‘dislike’. These responses were summarised using word clouds, frequency tables and through sentiment scores to reveal an overall positive attitude to VF. Sentiment scores enabled the identification of consumer segments with interpretable differences in their attitude towards VF. Comparison with direct attitude questions and consumer profiling contributed to method validation and further confirmed the potential of this new method. For the former validation approach, the sentiment of specific sentences in the text highlighting task matched results from self-reported attitudes based on Likert scales. Consumer profiling showed that segments with different sentiment scores in the text highlighting task differed in their food technology neophobia scores in the expected direction. Future research should investigate methodological aspects of text highlighting and explore its suitability to other applications.

Research on the Art Value and Application of Art Creation Based on the Emotion Analysis of Art

The ultimate embodiment of the value of the art creation process is artistic value, which is the embodiment of the greatest value created by art. Art creation is a form of art culture expression. To make their works more cultural and artistic, creators incorporate their personal creative style and ideological concepts. The ultimate expression of the value of the art creation process is artistic value, which is the embodiment of the greatest value of art creation. It provides a useful method for conducting digital research on human artistic works and has important implications for the protection and innovation of such works. In order to better realize artistic work research and innovation, this article primarily organizes and analyzes the literature on art classification and sentiment analysis currently available in the United States and abroad. This paper proposes a Python-based machine learning art emotion analysis method to investigate the issue of art emotion analysis. This program can achieve better results in analyzing sentiment orientation through a large number of experiments, and it is more efficient than a traditional weighted art sentiment analysis algorithm. This paper proposes a conditional random field extraction of core sentences-based art sentiment analysis algorithm for long works of art. The conditional random field is used to locate evaluation objects from which core sentences can be extracted, and an algorithm for sentiment sentence emotional polarity weight synthesis is proposed. Finally, experiments are used to compare the algorithm. The algorithm’s stability and effectiveness are demonstrated by its accuracy, recall, and F-value.

Using SentiWordNet and Sentiment Analysis for Evaluating Adverbs of Manner

In this paper, we examine the extent to which adverbs are, in themselves, sentiment-laden, the effect they have on the words they modify as well as the sentiment of sentences they appear in as a whole and consider the sentiment scores as listed from SentiWordNet in relation to definitions laid out in WordNet. We examined 100 adverbs of manner, comparing their definitions as laid out in WordNet with their sentiment scores as given in SentiWordNet. It was concluded that adverbs of manner are in themselves sentiment-laden and that there is a significant-enough disparity between the definitions and the sentiment scores to introduce errors in SentiWordNet-based automated sentiment evaluations of sentences.

Understand Students Feedback Using Bi-Integrated CRF Model Based Target Extraction

Educational institutions showing interest to find the opinion of the students about their course and the instructors to enhance the teaching-learning process. For this, most research uses sentiment analysis to track students’ behavior. Traditional sentence-level sentiment analysis focuses on the whole sentence sentiment. Previous studies show that the sentiments alone are not enough to observe the feeling of the students because different words express different sentiments in a sentence. There is a need to extract the targets in a given sentence which helps to find the sentiment towards those targets. Target extraction is the subtask of targeted sentiment analysis. In this paper, we proposed the innovative model to find the targets of the given sentence using Bi-Integrated Conditional Random Fields (CRF). A Parallel fusion neural network model is designed to perform this task. We evaluate the model using the Michigan dataset and we build a dataset for target extraction from student reviews. The experimental results show that our proposed fusion model achieves better results compared to baseline models.

A Cross-Lingual Sentence Similarity Calculation Method With Multifeature Fusion

Cross-language sentence similarity computation is among the focuses of research in natural language processing (NLP). At present, some researchers have introduced fine-grained word and character features to help models understand sentence meanings, but they do not consider coarse-grained prior knowledge at the sentence level. Even if two cross-linguistic sentence pairs have the same meaning, the sentence representations extracted by the baseline approach may have language-specific biases. Considering the above problems, in this paper, we construct a Chinese–Uyghur cross-lingual sentence similarity dataset and propose a method to compute cross-lingual sentence similarity by fusing multiple features. The method is based on the cross-lingual pretraining model XLM-RoBERTa and assists the model in similarity calculation by introducing two coarse-grained prior knowledge features, i.e., sentence sentiment and length features. At the same time, to eliminate possible language-specific biases in the vectors, we whitened the sentence vectors of different languages to ensure that they were all represented under the standard orthogonal basis. Considering that the combination of different vectors has different effects on the final performance of the model, we introduce different vector features for comparison experiments based on the basic feature splicing method. The results show that the absolute value feature of the difference between two vectors can reflect the similarity of two sentences well. The final F1 value of our method reaches 98.97%, which is 19.81% higher than that of the baseline.

MAI-CBert: Multidirectional Attention Interaction Construction-Bert Sentiment Sentence Representation

Considering that when the traditional network structure is connected with the pre-trained language model for sentence embedding representation, it relies on simple word weights to generate sentence vectors, which easily ignores the global and contextual semantic details of sentences, reducing the accuracy of sentence representation. To address these issues, we develop a Multidirectional Attention Interaction Construction-Bert Sentence Representation Framework (MAI-CBert). First, the model uses deep attention to transform the input emotional sentences to reduce misunderstanding caused by the unbalanced distribution of word weights in the sentences; at the same time, it applies horizontal and vertical attention to emotional constructions, which are generated from two different directions. Construction vectors, aim to focus on words with salient features in sentences and establish close dependencies. Second, the dynamic interaction strategy is adopted to realize the interaction between attention in different directions, so that the information flow forms a complementary relationship, resulting in more effective sentence vectors. Notably, the loss function is refactored to improve the representation robustness of the model to avoid catastrophic forgetting. Experimental results on the SemEval-14 and ACL-14 baseline datasets demonstrate that the MAI-CBert sentence representation framework is robust and competitive.

BiERU: Bidirectional emotional recurrent unit for conversational sentiment analysis

Sentiment analysis in conversations has gained increasing attention in recent years for the growing amount of applications it can serve, e.g., sentiment analysis, recommender systems, and human-robot interaction. The main difference between conversational sentiment analysis and single sentence sentiment analysis is the existence of context information that may influence the sentiment of an utterance in a dialogue. How to effectively encode contextual information in dialogues, however, remains a challenge. Existing approaches employ complicated deep learning structures to distinguish different parties in a conversation and then model the context information. In this paper, we propose a fast, compact and parameter-efficient party-ignorant framework named bidirectional emotional recurrent unit for conversational sentiment analysis. In our system, a generalized neural tensor block followed by a two-channel classifier is designed to perform context compositionality and sentiment classification, respectively. Extensive experiments on three standard datasets demonstrate that our model outperforms the state of the art in most cases.

Attention-based position-aware framework for aspect-based opinion mining using bidirectional long short-term memory

Aspect-based Opinion Mining is a form of fine-grained Sentiment Analysis and it models the semasiological relationship between aspect terms and context words in a sentence. The presence of a variety of context words has a significant impact on a sentence's sentiment polarity. As a result, while designing a model, it is necessary to consider the interaction of aspects and context words. Although existing approaches have taken into account an aspect’s position in a sentence, much of the research works have not explored the use of Sentiment Lexicons with the Deep Learning algorithms. In this paper, we propose a framework for an Attention-based position-aware Bidirectional Long Short-Term Memory network for Aspect-based Opinion Mining that incorporates a Sentiment Intensity Lexicon. The aspect word’s pre-trained vector is adjusted to be closer to semantically and sentimentally similar nearest neighbors and further away from sentimentally dissimilar neighbors. The proposed framework calculates aspect weights by concatenating the external knowledge in the form of lexicon sentiment intensity scores with word embeddings and position information. The framework experiments on the SemEval 2014 dataset. The results of the experiments illustrate that injecting external knowledge into the Bidirectional Long Short-Term Memory network can improve classification accuracy significantly.

Text highlighting as a new way of measuring consumers' attitudes: A case study on vertical farming

Attitude measurement occupies a central position in consumer research. Concerns over the validity and reliability of traditional measures have motivated the development of alternative approaches. The present research introduces text highlighting as a method for measurement of explicit attitudes using a case study on vertical farming (VF) with 837 UK consumers. They participated in an online survey, where they read a text about VF and used highlighting functions to mark text as ‘like’ and ‘dislike.’ Consumers approached the task in a systematic and logical way and desirable aspects of VF were frequently highlighted as ‘like’, whereas undesirable aspects were more frequently highlighted as ‘dislike’. The text highlighting responses were summarised using word clouds, frequency tables and through sentiment scores to reveal an overall positive attitude to VF among participants. Sentiment scores enabled the identification of consumer segments with interpretable differences in their attitude towards VF. Two approaches to method validation – comparison with direct attitude questions and consumer profiling – further confirmed the potential of the text highlighting method. The sentiment of specific sentences in the text highlighting task matched results from self-reported attitudinal based on Likert scales. Consumer segments with different sentiment in the text highlighting task also differed in their food technology neophobia scores in the expected direction. Future research should investigate methodological aspects of text highlighting and explore its suitability to other applications.

Using Latent Fine-Grained Sentiment for Cross-Domain Sentiment Analysis

The authors investigate the problem task of multi-source cross-domain sentiment classification under the constraint of little labeled data. The authors propose a novel model which is capable of capturing both sentiment terms with strong or weak polarity from various source domains which are useful for knowledge transfer to unlabeled target domain. The authors propose a two-step training strategy with different granularities helping the model to identify sentiment terms with different degrees of sentiment polarity. Specifically, the coarse-grained training step captures the strong sentiment terms from the whole review while the fine-grained training step focuses on the latent fine-grained sentence sentiment which are helpful under the constraint of little labeled data. Experiments on a real-world product review dataset show that the proposed model has a good performance even under the little labeled data constraint.

Differences in Chinese and Western tourists faced with Japanese hospitality: a natural language processing approach

Since culture influences expectations, perceptions, and satisfaction, a cross-culture study is necessary to understand the differences between Japan's biggest tourist populations, Chinese and Western tourists. However, with ever-increasing customer populations, this is hard to accomplish without extensive customer base studies. There is a need for an automated method for identifying these expectations at a large scale. For this, we used a data-driven approach to our analysis. Our study analyzed their satisfaction factors comparing soft attributes, such as service, with hard attributes, such as location and facilities, and studied different price ranges. We collected hotel reviews and extracted keywords to classify the sentiment of sentences with an SVC. We then used dependency parsing and part-of-speech tagging to extract nouns tied to positive adjectives. We found that Chinese tourists consider room quality more than hospitality, whereas Westerners are delighted more by staff behavior. Furthermore, the lack of a Chinese-friendly environment for Chinese customers and cigarette smell for Western ones can be disappointing factors of their stay. As one of the first studies in the tourism field to use the high-standard Japanese hospitality environment for this analysis, our cross-cultural study contributes to both the theoretical understanding of satisfaction and suggests practical applications and strategies for hotel managers.

Learning sentiment sentence representation with multiview attention model

Self-attention mechanisms in deep neural networks, such as CNN, GRU and LSTM, have been proven to be effective for sentiment analysis. However, existing attention models tend to focus on individual tokens or aspect meanings in an expression. If a text contains information on multiple sentiments from different perspectives, the existing models will fail to extract the most critical and comprehensive features of the whole text. In the present study, a multiview attention model was proposed for learning sentence representation. Instead of using a single attention, multiple view vectors were used to map the attentions from different perspectives. Then, a fusion gate was adopted to combine these multiview attentions to draw a conclusion. To ensure the differences between multiview attentions, a regularization item was introduced to add a penalty to the loss function. In addition, the proposed model can be extended to other text tasks, such as questions and topics, to provide a comprehensive representation for the classification. Comparative experiments were conducted on both multiclass and multilabel classification datasets. The results revealed that the proposed method improves the performance of several previously proposed attention models.

Sentiment Lossless Summarization

The aim of automatic text summarization (ATS) is to extract representative texts from documents and keep major points of the extracted texts consistent with the original documents. However, most existing studies ignore sentimental information loss in the summarization process, which leads to sentiment loss summarization. To address the sentiment loss issue during summarization, we introduce a sentiment compensation mechanism into document summarization and propose a graph-based extractive summarization approach named Sentiment Lossless Summarization (SLS). SLS first creates a graph representation for a document to obtain the importance score (i.e., literal indicator) of each sentence. Second, sentiment dictionaries are leveraged to analyze the sentence sentiments. Third, during each summarization iteration, the sentences with the lowest scores are iteratively removed, and the sentiment compensation weights of the remaining sentences are updated. With the help of sentiment compensation during the summarization process, sentiment consistencies between candidate summaries and the original documents are maintained. Intrinsic evaluations conducted on the DUC2001, DUC2002, DUC2004, and Multi-News datasets demonstrate that our approach outperforms baselines and state-of-the-art summarization methods in terms of Recall-Oriented Understudy for Gisting Evaluation (ROUGE) scores. Additionally, to further evaluate SLS performance in sentiment retention, extrinsic evaluations are introduced, and summary quality in terms of sentiment loss is evaluated by measuring the prediction accuracy for sentiment polarities of either movie (IMDb dataset) or product (Amazon dataset) review summaries. The experimental results demonstrate that our approach can improve prediction accuracy by at most 6% compared to the baseline.

Improving Accuracy of The Sentence-Level Lexicon-Based Sentiment Analysis Using Machine Learning

Sentiment Analysis studies people's attitudes, opinions, evaluations, emotions, sentiments toward some entities such as products, topics, individuals, services, issues and classify them whether the opinion or evaluations inclines to that entities or not. It is getting more research focus in recent years due to its benefits for scientific and commercial purposes. This research aims at developing a better approach for sentiment analysis at the sentence level by using a combination of lexicon resources and a machine learning method. Moreover, as reviews data on the internet is unstructured and has much noise, this research uses different preprocessing techniques to clean the data before processing in different algorithms discussed in subsequent sections. Additionally, the lexicon building processes, how the lexicon is handled and combined with the machine learning algorithm for predicting sentiment is also discussed. In sentiment analysis, sentence's sentiment can be classified into three classes: positive sentiment, negative sentiment, or neutral. However, in this research work, we have excluded neutral sentiment for avoiding ambiguity and unnecessary complexity. The experiment results show that the proposed algorithm outperforms compared to the baseline machine learning algorithms. We have used four distinct datasets and different performance measures to check and validate the proposed method's robustness.