Text-based Sentiment Analysis Research Articles

Hybrid deep learning approach for sentiment analysis using text and emojis.

Sentiment Analysis (SA) is a technique for categorizing texts based on the sentimental polarity of people's opinions. This paper introduces a sentiment analysis (SA) model with text and emojis. The two preprocessed data's are data with text and emojis and text without emojis. Feature extraction consists text features and text with emojis features. The text features are features like N-grams, modified Term Frequency-Inverse Document Frequency (TF-IDF), and Bag-of-Words (BoW) features extracted from the text. In classification, CNN (Conventional Neural Network) and MLP (Multi-Layer Perception) use emojis and text-based SA. The CNN weight is optimized by a new Electric fish Customized Shark Smell Optimization (ECSSO) Algorithm. Similarly, the text-based SA is carried out by hybrid Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) classifiers. The bagged data are given as input to the classification process via RNN and LSTM. Here, the weight of LSTM is optimized by the suggested ECSSO algorithm. Then, the mean of LSTM and RNN determines the final output. The specificity of the developed scheme is 29.01%, 42.75%, 23.88%,22.07%, 25.31%, 18.42%, 5.68%, 10.34%, 6.20%, 6.64%, and 6.84% better for 70% than other models. The efficiency of the proposed scheme is computed and evaluated.

Analyzing Public Sentiment Regarding the Qatar 2023 World Cup Debate Using TF-IDF and K-Nearest Neighbor Weighting

This research aims to uncover the sentiment of Twitter users regarding the polemics surrounding the 2023 Qatar World Cup using a text-based sentiment analysis approach. The research methodology involves collecting data from Twitter posts, encompassing discussions, opinions, and responses related to the Qatar World Cup 2023. The TF-IDF weighting is applied to identify significant keywords in each post, while the K-Nearest Neighbor algorithm is employed to classify sentiments as positive, negative, or neutral. The findings reveal a comprehensive picture of how the public perceives the Qatar World Cup 2023 on the Twitter platform. The results not only cover positive and negative aspects of online discussions but also identify trends and patterns of sentiment that emerge during specific periods.The application of these methods provides valuable insights into understanding the dynamics of public opinion related to international sports events through the lens of social media. The results of the analysis demonstrate that a majority of Twitter users express positive sentiments towards the Qatar World Cup 2023, highlighting excitement and anticipation. However, some negative sentiments also arise, primarily related to controversies and concerns about the event. The research further identifies temporal variations in sentiment, reflecting changing public perceptions over time.This research contributes to the development of sentiment analysis methods by using a combination of TF-IDF weighting and the K-Nearest Neighbor algorithm to delve into Twitter users' perspectives. Consequently, the findings have practical applicability for further research and implementation in managing the social impact and public perception of major sporting events like the World Cup. .

A CONSUMER SENTIMENT ANALYSIS METHOD BASED ON EEG SIGNALS AND A RESNEXT MODEL

E-commerce is becoming increasingly dependent on technologies such as consumer sentiment research at an ever-increasing rate. Its purpose is to recognize and comprehend the feelings and dispositions of customers by analyzing customer language and behavior as expressed in social media, online reviews, and other forms of digital communication. The proliferation of digital technology has resulted in an increase in the number and variety of channels through which customers can communicate their feelings. Gaining a comprehensive understanding of consumer sentiment may be of great use to businesses, as it enables them to better satisfy customer demands, enhance their products and services, improve their brand reputation, and ultimately increase their level of competitiveness. As a result, consumer sentiment research has evolved into a tool that is essential for the decision-making process in e-commerce as well as the management of customer relationships. Within the scope of this discussion, this study uses deep learning models to improve consumer sentiment research precision. The following is the list of the primary contributions that this paper makes. (1) Advancing the use of EEG signals as a basis for a method for analyzing customer feelings. This technique measures brain activity directly, thus avoiding the restrictions and ambiguities that come with relying on verbal expression. (2) The purpose of this study is to improve the overall performance of the model for analyzing sentiment by incorporating an attention mechanism into the ResNeXt model. This attention mechanism is intended to augment the model’s capacity to extract subtle characteristics. (3) The results of the experiments show that the strategy described in this study is effective in improving EEG-based sentiment analysis performance. When compared to standard text-based sentiment analysis approaches, this sentiment analysis model demonstrates greater objectivity, real-time capabilities, and multidimensionality when applied to consumer sentiment analysis in e-commerce.

A pragmatic analysis of text-based sentiment analysis models from an analytical perspective

NLP sentiment analysis involves pre-processing, feature extraction, feature selection, sentiment classification, and post-processing. Pre-processing can include PoS tagging, stopword removal, language-specific missing work detection and correction, etc. Word2vec, N Gramme, term frequency (TF), distance measures, etc. can extract and select features. CNNs, DNNs, and other machine learning and deep learning models can be used for classification and post-processing. These models form a sentiment analysis engine, which can be assessed for accuracy, precision, recall, analysis delay, computational complexity, etc. The wide variation in algorithmic combination and performance makes it difficult for researchers to choose the best model(s) for their application. This task requires a lot of model testing and validation, which increases time-to-market and product cost. This text surveys recently proposed sentiment analysis models to reduce ambiguity and speed up system development. This survey characterises reviewed models’ nuances, advantages, limitations, and future research scopes to help readers choose the best models for their application deployment. These models’ delay, accuracy, precision, recall, computational complexity, and probable use are also compared in this text. This comparison lets readers choose the best model(s) for their application deployment and use them without validation. Reducing testing delay will improve model performance for system design and deployment for multiple sentiment analysis applications

Improving sentiment classification using a RoBERTa-based hybrid model.

Several attempts have been made to enhance text-based sentiment analysis's performance. The classifiers and word embedding models have been among the most prominent attempts. This work aims to develop a hybrid deep learning approach that combines the advantages of transformer models and sequence models with the elimination of sequence models' shortcomings. In this paper, we present a hybrid model based on the transformer model and deep learning models to enhance sentiment classification process. Robustly optimized BERT (RoBERTa) was selected for the representative vectors of the input sentences and the Long Short-Term Memory (LSTM) model in conjunction with the Convolutional Neural Networks (CNN) model was used to improve the suggested model's ability to comprehend the semantics and context of each input sentence. We tested the proposed model with two datasets with different topics. The first dataset is a Twitter review of US airlines and the second is the IMDb movie reviews dataset. We propose using word embeddings in conjunction with the SMOTE technique to overcome the challenge of imbalanced classes of the Twitter dataset. With an accuracy of 96.28% on the IMDb reviews dataset and 94.2% on the Twitter reviews dataset, the hybrid model that has been suggested outperforms the standard methods. It is clear from these results that the proposed hybrid RoBERTa-(CNN+ LSTM) method is an effective model in sentiment classification.

Sentiment analysis of Indonesian datasets based on a hybrid deep-learning strategy

Various attempts have been conducted to improve the performance of text-based sentiment analysis. These significant attempts have focused on text representation and model classifiers. This paper introduced a hybrid model based on the text representation and the classifier models, to address sentiment classification with various topics. The combination of BERT and a distilled version of BERT (DistilBERT) was selected in the representative vectors of the input sentences, while the combination of long short-term memory and temporal convolutional networks was taken to enhance the proposed model in understanding the semantics and context of each word. The experiment results showed that the proposed model outperformed various counterpart schemes in considered metrics. The reliability of the proposed model was confirmed in a mixed dataset containing nine topics.

Social informedness and investor sentiment in the GameStop short squeeze.

We examine investor behavior on social media platforms related to the GameStop (GME) short squeeze in early 2021. Individual investors stimulated the stock market via Reddit social posts in the presence of institutional investors who bet against GME's success as short sellers. We analyzed r/WallStreetBets subreddit posts related to GME's trading patterns. We performed text-based sentiment analysis and compared the social informedness of posting users for GME trading on two social media platforms. The short squeeze occurred due to coordinated trading by individual investors, who discussed trading strategies on the platforms and drove collective social informedness-based trading behavior. Our findings suggest that the valence and number of submissions influenced GME's intraday transaction volumes and precursors for irrational trading behavior patterns to have emerged. We provide a theoretical interpretation of what occurred and call for tighter monitoring of social news platforms. We also encourage effort to create an in-depth understanding of the observed patterns and the linkages between them and the larger equity markets.

A multimodal sentiment analysis system for recognizing person aggressiveness in pain based on textual and visual information

This article proposes a multimodal sentiment analysis system for recognizing a person’s aggressiveness in pain. The implementation has been divided into five components. The first three steps are related to a text-based sentiment analysis system to perform classification tasks such as predicting the classes into non-aggressive, covertly aggressive, and overtly aggressive classes. The remaining two components are related to an image-based sentiment analysis system. A deep learning-based approach has been employed to do feature learning and predict the three types of pain classes. An aggression dataset for the text-based system and the UNBC-McMaster database for an image-based system has been employed, respectively. Experimental results have been compared with the state-of-the-art methods, showing the superiority of the proposed approach. Finally, the scores due to text-based and image-based sentiment analysis systems are fused to obtain the performance for the proposed multimodal sentiment analysis system.

To See the Invisible: An Empirical Comparison of Methods for Text-Based Sentiment Analysis of Online Contents From People With Autism Spectrum Condition

With the growing awareness of autism, more and more organizations tried to adapt their service offerings to the special needs of autistic groups. However, the extant relevant research in marketing is still limited, and lacks empirical evidence from the field as well as affective measures. Thus, we explore a secondary research method that is able to capture affective states of autistic people. This research aims to solve two critical assumption questions relevant to the feasibility to utilize online secondary data here: (1) How to efficiently discriminate autistic groups from neurotypical people among a huge amount of unstructured online data, given that the autistic identity is usually unlabeled and invisible? (2) Do the online contents by autistic groups provide good diagnosticity for sentiment analysis, given that many studies in this area doubted the emotional processing ability of autistic people? In our case study, we focused on people who were diagnosed with autism and successfully identified 664 autistic individuals, and then collected their user-generated content on Reddit. In the end, we collected 9305 sentences in 842 posts for sentiment analysis. Based on the results of three content analysis methods in our case study, we recommend a hybrid method that involves a collaboration between human coders and AI-enabled analysis. We provide a detailed procedure about how to implement this method, and also metrics that help to evaluate the analysis results. This method can significantly improve the efficiency of the coding process with an acceptable loss of data points, which solves the first assumption question. This paper further shows that autistic groups would provide emotional information in an online environment but in a different behavioral pattern at the aggregate level, which solves the second assumption question and is initial evidence to encourage future empirical research on autistic consumers to include emotional factors.

A novel text sentiment analysis system using improved depthwise separable convolution neural networks.

Human behavior is greatly affected by emotions. Human behavior can be predicted by classifying emotions. Therefore, mining people's emotional tendencies from text is of great significance for predicting the behavior of target groups and making decisions. The good use of emotion classification technology can produce huge social and economic benefits. However, due to the rapid development of the Internet, the text information generated on the Internet increases rapidly at an unimaginable speed, which makes the previous method of manually classifying texts one-by-one more and more unable to meet the actual needs. In the subject of sentiment analysis, one of the most pressing problems is how to make better use of computer technology to extract emotional tendencies from text data in a way that is both more efficient and accurate. In the realm of text-based sentiment analysis, the currently available deep learning algorithms have two primary issues to contend with. The first is the high level of complexity involved in training the model, and the second is that the model does not take into account all of the aspects of language and does not make use of word vector information. This research employs an upgraded convolutional neural network (CNN) model as a response to these challenges. The goal of this model is to improve the downsides caused by the problems described above. First, the text separable convolution algorithm is used to perform hierarchical convolution on text features to achieve the refined extraction of word vector information and context information. Doing so avoids semantic confusion and reduces the complexity of convolutional networks. Secondly, the text separable convolution algorithm is applied to text sentiment analysis, and an improved CNN is further proposed. Compared with other models, the proposed model shows better performance in text-based sentiment analysis tasks. This study provides great value for text-based sentiment analysis tasks.

Beyond Sentiment Analysis: A Review of Recent Trends in Text Based Sentiment Analysis and Emotion Detection

Sentiment Analysis is probably one of the best-known area in text mining. However, in recent years, as big data rose in popularity more areas of text classification are being explored. Perhaps the next task to catch on is emotion detection, the task of identifying emotions. This is because emotions are the finer grained information which could be extracted from opinions. So besides writer sentiments, writer emotion is also a valuable data. Emotion detection can be done using text, facial expressions, verbal communications and brain waves; however, the focus of this review is on text-based sentiment analysis and emotion detection. The internet has provided an avenue for the public to express their opinions easily. These expressions not only contain positive or negative sentiments, it contains emotions as well. These emotions can help in social behaviour analysis, decision and policy makings for companies and the country. Emotion detection can further support other tasks such as opinion mining and early depression detection. This review provides a comprehensive analysis of the shift in recent trends from text sentiment analysis to emotion detection and the challenges in these tasks. We summarize some of the recent works in the last five years and look at the methods they used. We also look at the models of emotion classes that are generally referenced. The trend of text-based emotion detection has shifted from the early keyword-based comparisons to machine learning and deep learning algorithms that provide more flexibility to the task and better performance.

Investor Sentiment Index: A Systematic Review

The Investor Sentiment Index (ISI) is widely regarded as a useful measure to gauge the overall mood of the market. Investor panic may result in contagion, causing failure in financial markets. Market participants widely use the ISI indicator to understand price fluctuations and related opportunities. As a result, it is imperative to systematically review the compiled literature on the subject. In addition to reviewing past studies on the ISI, this paper attempts a bibliometric analysis (BA) to understand any related publications. We systematically review over 100 articles and carry out a BA on a set of information based on the publication year, the journal, the countries/territories, the deployed statistical tools and techniques, a citation analysis, and a content analysis. This analysis further strengthens the study by establishing interesting findings. Most articles use the Baker and Wurgler index and text-based sentiment analysis. However, an Internet-search-based ISI was also used in a few of the studies. The results reveal the lack of direct measures or a robust qualitative approach in constructing the ISI. The findings further indicate a vast research gap in emerging economies, such as India’s. This study had no limit on the period for inclusion and exclusion. We believe that our current work is a seminal study, jointly involving a systematic literature review and BA, that will enormously facilitate academicians and practitioners working on the ISI.

Sentiment Analysis of Facebook Posts through Special Reactions: The Case of Learning from Home in Indonesia Amid COVID-19

In contrast to several other countries, Indonesian sentiment analysis research is primarily focused on the text-based analysis of Twitter. Given that Twitter users in Indonesia account for less than a seventh of those on Facebook, sentiment analysis on the latter may have a greater impact than on the former. This research sought to close that gap in the literature by pioneering the use of Facebook special reactions as an alternative to text-based sentiment analysis on social media posts about Indonesian social issues. The topic of learning from home in the midst of the COVID-19 pandemic was chosen because it is both timely and relatable to almost everyone in the country. Through CrowdTangle, a total of 39,657 Facebook posts containing the key phrase “belajar dari rumah” were gathered, but only 9,310 of them received special reactions and thus remained to be analyzed quantitatively. The results indicated that with the exception of ‘love,’ all special reactions are somewhat correlated, suggesting that they can be used to indicate the negative valence of a Facebook post. Further analysis revealed a significant increase in the proportion of posts with a negative valence during the second year of the COVID-19 pandemic. The textual analysis of the posts revealed that those with a negative valence primarily discuss internet access and other IT infrastructure issues that presumably impede learning from home activities for some. The main contribution of this study is to demonstrate how to analyze special reactions on Facebook for sentiment analysis purposes, particularly in the context of Indonesia. Additionally, it lays out how Facebook's special reactions have the potential to be used in conjunction with text-based sentiment analysis to provide a complete picture of the social issue being investigated.

U.S. banks’ lending, financial stability, and text-based sentiment analysis

We examine the impact of investor sentiment on bank credit and financial stability. We also investigate how loan growth may affect bank stability. We use a large panel data set of U.S. commercial banks over the period 1999Q1–2015Q4, using bank-level data. Investor sentiment is proxied by two novel but alternative measures based on textual analysis. First, we employ the sentiment measure constructed by García (2013) based on the fraction of positive and negative words in two columns of financial news from the New York Times. Second, we employ the text-based measure of uncertainty constructed by Manela and Moreira (2017) called News Implied Volatility, which uses front-page articles of the Wall Street Journal. The results show that banks’ lending falls when investor sentiment is low, while this effect is more pronounced when banks hold a higher level of credit risk. These effects are more pronounced during recessions with loan growth also responding negatively to the anxiety of investors throughout recessions. Finally, we show that the Great Financial Crisis had a negative effect on investor sentiment leading to a decline in U.S. lending behavior and an increase in the U.S. banking sector instability.

An empirical comparison of machine learning methods for text-based sentiment analysis of online consumer reviews

The amount of digital text-based consumer review data has increased dramatically and there exist many machine learning approaches for automated text-based sentiment analysis. Marketing researchers have employed various methods for analyzing text reviews but lack a comprehensive comparison of their performance to guide method selection in future applications. We focus on the fundamental relationship between a consumer’s overall empirical evaluation, and the text-based explanation of their evaluation. We study the empirical tradeoff between predictive and diagnostic abilities, in applying various methods to estimate this fundamental relationship. We incorporate methods previously employed in the marketing literature, and methods that are so far less common in the marketing literature. For generalizability, we analyze 25,241 products in nine product categories, and 260,489 reviews across five review platforms. We find that neural network-based machine learning methods, in particular pre-trained versions, offer the most accurate predictions, while topic models such as Latent Dirichlet Allocation offer deeper diagnostics. However, neural network models are not suited for diagnostic purposes and topic models are ill equipped for making predictions. Consequently, future selection of methods to process text reviews is likely to be based on analysts’ goals of prediction versus diagnostics.

Age-Related Differences in Affective Norms for Chinese Words (AANC).

Information on age-related differences in affective meanings of words is widely used by researchers to study emotions, word recognition, attention, memory, and text-based sentiment analysis. To date, no Chinese affective norms for older adults are available although Chinese as a spoken language has the largest population in the world. This article presents the first large-scale age-related affective norms for 2,061 four-character Chinese words (AANC). Each word in this database has rating values in the four dimensions, namely, valence, arousal, dominance, and familiarity. We found that older adults tended to perceive positive words as more arousing and less controllable and evaluate negative words as less arousing and more controllable than younger adults did. This indicates that the positivity effect is reliable for older adults who show a processing bias toward positive vs. negative words. Our AANC database supplies valuable information for researchers to study how emotional characteristics of words influence the cognitive processes and how this influence evolves with age. This age-related difference study on affective norms not only provides a tool for cognitive science, gerontology, and psychology in experimental studies but also serves as a valuable resource for affective analysis in various natural language processing applications.

Naive Bayes Classifier Optimization on Sentiment Analysis of Hotel Reviews

Feature extraction plays an important role in the sentiment analysis process, especially of text data. The Naive Bayes Classifier performs well on low feature dimensions. However, the accuracy provided is not optimal. To acquire optimal machine learning model, information gain method, evolutionary algorithm, and swarm intelligent algorithm are applied. The objective of this study is to determine the performance of the Particle Swarm Optimization (PSO) to optimize the Naive Bayes Classifier. Vectorization of words is carried out using TF-IDF. In order to produce high PSO performance, the PSO-NBC model is tested with several parameters, namely the number of particles (k = 3), setting of the number of iterations and inertia weight, individual intelligence coefficient (c1 = 1), and social intelligence coefficient (c2 = 2). Inert weight is calculated using the formulation (w = 0.5+ Rand ([- 1,1])). In conclusion, PSO is able to solve the problem space of text-based sentiment analysis. PSO is able to optimize the accuracy of Naive Bayes at a value of 89% to 91.76%. PSO performance is determined by the parameters used, especially the number of particles, the number of iterations, and the weight of inertia. A large number of particles accompanied by an increase in inertia weight can increase accuracy. The number of particles 20-30 has reached the optimal accuracy.

Naive Bayes Classifier Optimization on Sentiment Analysis of Hotel Reviews

<p class="JGI-AbstractIsi">Feature extraction plays an important role in the sentiment analysis process, especially of text data. The Naive Bayes Classifier performs well on low feature dimensions. However, the accuracy provided is not optimal. To acquire optimal machine learning model, information gain method, evolutionary algorithm, and swarm intelligent algorithm are applied. The objective of this study is to determine the performance of the Particle Swarm Optimization (PSO) to optimize the Naive Bayes Classifier. Vectorization of words is carried out using TF-IDF. In order to produce high PSO performance, the PSO-NBC model is tested with several parameters, namely the number of particles (k = 3), setting of the number of iterations and inertia weight, individual intelligence coefficient (c1 = 1), and social intelligence coefficient (c2 = 2). Inert weight is calculated using the formulation (w = 0.5+ Rand ([- 1,1])). In conclusion, PSO is able to solve the problem space of text-based sentiment analysis. PSO is able to optimize the accuracy of Naive Bayes at a value of 89% to 91.76%. PSO performance is determined by the parameters used, especially the number of particles, the number of iterations, and the weight of inertia. A large number of particles accompanied by an increase in inertia weight can increase accuracy. The number of particles 20-30 has reached the optimal accuracy.</p>

Multi‐level Deep Correlative Networks for Multi‐modal Sentiment Analysis

Multi-modal sentiment analysis (MSA) is increasingly becoming a hotspot because it extends the conventional Sentiment analysis (SA) based on texts to multi-modal content which can provide richer affective information. However, compared with textbased sentiment analysis, multi-modal sentiment analysis has much more challenges, because the joint learning process on multi-modal data requires both fine-grained semantic matching and effective heterogeneous feature fusion. Existing approaches generally infer sentiment type from splicing features extracted from different modalities but neglect the strong semantic correlation among cooccurrence data of different modalities. To solve the challenges, a multi-level deep correlative network for multimodal sentiment analysis is proposed, which can reduce the semantic gap by analyzing simultaneously the middlelevel semantic features of images and the hierarchical deep correlations. First, the most relevant cross-modal feature representation is generated with Multi-modal Deep and discriminative correlation analysis (Multi-DDCA) while keeping those respective modal feature representations to be discriminative. Second, the high-level semantic outputs from multi-modal deep and discriminative correlation analysis are encoded into attention-correlation cross-modal feature representation through a co-attention-based multimodal correlation submodel, and then they are further merged by multi-layer neural network to train a sentiment classifier for predicting sentimental categories. Extensive experimental results on five datasets demonstrate the effectiveness of the designed approach, which outperforms several state-of-the-art fusion strategies for sentiment analysis.

A Practical Application for Text-Based Sentiment Analysis Based on Bayes-LSTM Model

Text-based sentiment analysis algorithms have now become one of the active research areas in emotional analysis which has gained much attention nowadays. Text emotion classification can be widely used in social public opinion analysis, product use feedback, harmful information filtering, etc. In this paper, we first developed a robotic crawler to gather data about comment on Huawei cellphone from Sina weibo microblog sites (Chinese twitter). Then we generate the data text to be trained according to the input requirements of the Keras module, and perform formal training and learning on the model after data preprocessing. Subsequently, the classifier was constructed based on the Bayes-LSTM model in which TF-IDF model was used for feature selection. The LSTM model can be characterized by the ability to self-evaluate the usefulness of the information obtained, which makes up for the shortcoming of naive Bayes formula that only applies to two independent events. We finally have a practical application that generates a word cloud from text, showing frequently used words in larger font sizes, effectiveness of the algorithm was also verified by experiment.