Sentiment Lexicon Research Articles

Protesting With Feeling in Puerto Rico: Twitter and El Verano Del 19

Communication research has extensively addressed the influence of social media on protest. We seek to add to this body of research by examining how sentiment contained in Twitter communication about protest can condition the public reach of this communication. Specifically, we are interested in whether Twitter communication couched in negative sentiments like anger, disgust, and fear will drive this reach more than communication couched in positive sentiment such as trust, joy, and surprise. We rely on sentiment analysis to examine roughly 30,000 tweets surrounding the summer of 2019 Protests in Puerto Rico. These protests centered on a scandal involving former Governor Ricardo Rossello that ultimately led to his resignation. The analysis required adapting an English language sentiment dictionary to Spanish. Our results suggest that protesters frequently tweeted with both positive and negative sentiment when calling for the governor’s resignation, but ultimately, those tweets couched in negative sentiment, when compared to those with positive sentiment, had the most reach. That said, those tweets including either positive or negative sentiment had more reach than those absent sentiment.

Investor Sentiment Analysis Based on Comment Text for Stock Price Prediction

The sentiment of stock investors is affected by many factors, and the trend of stock price is reflected by the decision behavior of stock trades. Previous tendency can be seen in technical index, and people's sentiment towards the stock is closely related to the text of commentary on the stock. This paper adopts sentiment dictionary to classify the same stock comment text, and modify the corpus of SnowNLP to train a model that conforms to Chinese stock comment. Based on the stock technical index and the established sentiment index to achieve the stock price prediction, so as to achieve a better analysis and prediction of the stock price trend. According to the analysis, the established investor sentiment index has a good effect on stock prediction. To be specific, the predictive regression score of the investor sentiment index is significantly improved compared with those of purely technical indicators, and MAE, MSE and other error indicators are obviously decreased. These results shed light on guiding further exploration of stock forecast with investor sentiment.

Sentiment annotations for 3827 simplified Chinese characters.

Sentiment analysis in Chinese natural language processing has been largely based on words annotated with sentiment categories or scores. Characters, however, are the basic orthographic, phonological, and in most cases, semantic units in the Chinese language. This study collected sentiment annotations for 3827 characters. The ratings demonstrated high levels of reliability, and were validated through a comparison with the ratings of some characters' word equivalents reported in a previous norming study. Relations with other lexico-semantic variables and character processing efficiency were investigated. Furthermore, analyses of the association between constituent character valence and word valence revealed semantic compositionality and sentiment fusion characteristic of larger Chinese linguistic units. These ratings for characters, expanding current Chinese sentiment lexicons, can be utilized for the purposes of more precise stimuli assessment in research on Chinese character processing and more efficient sentiment analysis equipped with annotations of single-character words.

A Study of Public Attitudes toward Shanghai's Image under the Influence of COVID-19: Evidence from Comments on Sina Weibo.

With the advent of the Internet era, Chinese users tend to choose to express their opinions on social media platforms represented by Sina Weibo. The changes in people's emotions toward cities from the microblogging texts can reflect the image of cities presented on mainstream social media, and thus target a good image of cities. In this paper, we collected microblog data containing "Shanghai" from 1 January 2019 to 1 September 2022 by Python technology, and we used three methods: Term Frequency-Inverse Document Frequency keyword statistics, Latent Dirichlet Allocation theme model construction, and sentiment analysis by Zhiwang Sentiment Dictionary. We also explore the impact of the COVID-19 epidemic on Shanghai's urban image in the context of the "Shanghai Territorial Static Management", an important public opinion topic during the COVID-19 epidemic. The results of the study show that the "Shanghai-wide static management" of COVID-19 epidemic has significantly reduced the public's perception of Shanghai and negatively affected the city's image. By analyzing the data results, we summarize the basic characteristics of Shanghai's city image and provide strategies for communicating Shanghai's city image in the post-epidemic era.

Sentiment analysis of medical record notes for lung cancer patients at the Department of Veterans Affairs.

Natural language processing of medical records offers tremendous potential to improve the patient experience. Sentiment analysis of clinical notes has been performed with mixed results, often highlighting the issue that dictionary ratings are not domain specific. Here, for the first time, we re-calibrate the labMT sentiment dictionary on 3.5M clinical notes describing 10,000 patients diagnosed with lung cancer at the Department of Veterans Affairs. The sentiment score of notes was calculated for two years after date of diagnosis and evaluated against a lab test (platelet count) and a combination of data points (treatments). We found that the oncology specific labMT dictionary, after re-calibration for the clinical oncology domain, produces a promising signal in notes that can be detected based on a comparative analysis to the aforementioned parameters.

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

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.

The colour of finance words

Our paper relies on stock price reactions to colour words, in order to provide new dictionaries of positive and negative words in a finance context. We extend the machine learning algorithm of Taddy (2013), adding a cross-validation layer to avoid over-fitting. In head-to-head comparisons, our dictionaries outperform the standard bag-of-words approach (Loughran and McDonald, 2011) when predicting stock price movements out-of-sample. By comparing their composition, word-by-word, our method refines and expands the sentiment dictionaries in the literature. The breadth of our dictionaries and their ability to disambiguate words using bigrams both help to colour finance discourse better.

Navigating Financial Complexity: Language Processing Insights for Modern Businesses

Complex financial markets generate massive volumes of unstructured text data from news, publications, social media, and analyst comments. NLP can assist investors and traders make real-time judgments using this data. Analysis of markets This article reviews NLP advancements in three main areas. Generally favorable, negative, or neutral opinions toward companies, industries, or markets. Conversational tone predicts market and investor confidence using machine learning and sentiment lexicons. Finance conversation topic discovery. NLP algorithms may find hidden patterns and investment possibilities by identifying frequently occurring keywords, phrases, and entities in vast text corpora. Classifying critical events like profits, mergers, and regulations. NLP canwarn traders and improve real-time trading decisions by gathering vital news and financial data.Sentiment analysis and transaction volume are used in stock market case studies. Market mood may affect trading performance, since numerical models link favorable sentiment to stock price increases. The importance of active trading in price changes is shown by regular trade volumes

Enabling an On-demand Access to Community Sentiments using LSTM RNNs Web Service Architecture

Analyzing community response has always played an important role in marketing and development of various products ranging from services to manufacturing. Analyzing the responses and interpreting them helps improve the quality of the product. This task is traditionally done by community managers using surveys and focus groups. One other hands-off solution would be using the technology to analyze sentiments from users’ digital interactions in various online communities. Sentiment Analysis is the use of Natural Language Processing to process text to identify and retrieve sentiments behind the textual data. Sentiment Analysis can be approached either by supervised classification where a machine learning model is trained using labeled data or lexicon-based unsupervised analysis where a sentiment dictionary is used to find the overall sentiment. The idea is to build a system which presents community sentiment results on discussion threads from reddit on demand with the end user not to concern about data processing. We propose a streamlined pipeline architecture for a web application with several closely connected components in the backend that allows users to simply send the reddit discussion thread's URL, which the backend receives and scrapes the comments using the Reddit API, creates a dataset, cleans it and performs sentiment analysis using a RNNs. We used a Long Short-Term Memory (LSTM) a variant of RNN like Bidirectional RNNs and Multi-layered RNNs are also used to get better results.

Discharge Summaries Based Sentiment Detection Using Multi-Head Attention and CNN-BiGRU

Automatic extraction of the patient’s health information from the unstructured data concerning the discharge summary remains challenging. Discharge summary related documents contain various aspects of the patient health condition to examine the quality of treatment and thereby help improve decision-making in the medical field. Using a sentiment dictionary and feature engineering, the researchers primarily mine semantic text features. However, choosing and designing features requires a lot of manpower. The proposed approach is an unsupervised deep learning model that learns a set of clusters embedded in the latent space. A composite model including Active Learning (AL), Convolutional Neural Network (CNN), BiGRU, and Multi-Attention, called ACBMA in this research, is designed to measure the quality of treatment based on discharge summaries text sentiment detection. CNN is utilized for extracting the set of local features of text vectors. Then BiGRU network was utilized to extract the text’s global features to solve the issues that a single CNN cannot obtain global semantic information and the traditional Recurrent Neural Network (RNN) gradient disappearance. Experiments prove that the ACBMA method can demonstrate the effectiveness of the suggested method, achieve comparable results to state-of-arts methods in sentiment detection, and outperform them with accurate benchmarks. Finally, several algorithm studies ultimately determined that the ACBMA method is more precise for discharge summaries sentiment analysis.

Thalassemia Screening by Sentiment Analysis on Social Media Platform Twitter

Thalassemia syndrome is a genetic blood disorder induced by the reduction of normal hemoglobin production, resulting in a drop in the size of red blood cells. In severe forms, it can lead to death. This genetic disorder has posed a major burden on public health wherein patients with severe thalassemia need periodic therapy of iron chelation and blood transfusion for survival. Therefore, controlling thalassemia is extremely important and is made by promoting screening to the general population, particularly among thalassemia carriers. Today Twitter is one of the most influential social media platforms for sharing opinions and discussing different topics like people’s health conditions and major public health affairs. Exploring individuals’ sentiments in these tweets helps the research centers to formulate strategies to promote thalassemia screening to the public. An effective Lexiconbased approach has been introduced in this study by highlighting a classifier called valence aware dictionary for sentiment reasoning (VADER). In this study applied twitter intelligence tool (TWINT), Natural Language Toolkit (NLTK), and VADER constitute the three main tools. VADER represents a gold-standard sentiment lexicon, which is basically tailored to attitudes that are communicated by using social media. The contribution of this study is to introduce an effective Lexicon-based approach by highlighting a classifier called VADER to analyze the sentiment of the general population, particularly among thalassemia carriers on the social media platform Twitter. In this study, the results showed that the proposed approach achieved 0.829, 0.816, and 0.818 regarding precision, recall, together with F-score, respectively. The tweets were crawled using the search keywords, “thalassemia screening,” thalassemia test, “and thalassemia diagnosis”. Finally, results showed that India and Pakistan ranked the highest in mentions in tweets by the public’s conversations on thalassemia screening with 181 and 164 tweets, respectively.

Feasible Sentiment Analysis of Real Time Twitter Data

Sentiment analysis plays a significant role in understanding public opinion, trends, and sentiments expressed on social media platforms. In this paper, we focus on performing sentiment analysis on real-time Twitter data to gain insights into the sentiments related to specific topics or events, we collect a stream of tweets based on predefined keywords or hashtags. The collected tweets undergo pre-processing steps to clean and standardize the text for sentiment analysis. We employ machine learning classify the sentiments expressed in tweets, utilizing sentiment lexicons and training data as references. Real-time sentiment analysis is performed as new tweets are collected, enabling continuous monitoring and analysis of public sentiment. The sentiment analysis results are visualized through informative visualizations such as sentiment distribution charts and sentiment trends over time. Additionally, we focus on topic-specific analysis by filtering tweets based on relevant keywords or hashtags, providing deeper insights into sentiments related to specific subjects. The paper faces challenges such as noisy and informal text, ambiguity in sentiment expression, and handling large volumes of real-time data. Addressing these challenges, we aim to develop an effective sentiment analysis system that provides valuable insights into public sentiment and supports decision-making processes in various domains.

Sentiment and Context-Aware Hybrid DNN With Attention for Text Sentiment Classification

A massive volume of unstructured data in the form of comments, opinions, and other sorts of data is generated in real-time with the growth of web 2.0. Due to the unstructured nature of the data, building an accurate predictive model for sentiment analysis remains a challenging task. While various DNN architectures have been applied to sentiment analysis with encouraging results, they suffer from high dimensional feature space and consider various features equally. State-of-the-art methods cannot properly leverage semantic and sentiment knowledge to extract meaningful relevant contextual sentiment features.This paper proposes a sentiment and context-aware hybrid DNN model with an attention mechanism that {intelligently learns and highlights salient features of relevant sentiment context in the text. We first use integrated wide coverage sentiment lexicons to identify text sentiment features then leverage bidirectional encoder representation from transformers to produce sentiment-enhanced word embeddings for text semantic extraction. After that, the proposed approach adapts the BiLSTM to capture both word order/contextual text semantic information and the long-dependency relation in the word sequence. Our model also employs an attention mechanism to assign weight to features and give greater significance to salient features in the word sequence. Finally, CNN is utilized to reduce the dimensionality of feature space and extract the local key features for sentiment analysis. The effectiveness of the proposed model is evaluated on real-world benchmark datasets demonstrating that the proposed model significantly improves the accuracy of existing text sentiment classification.

A Stock Price Prediction Model Based on Investor Sentiment and Optimized Deep Learning

Accurate prediction of stock prices can reduce investment risks and increase returns. This paper combines the multi-source data affecting stock prices and applies sentiment analysis, swarm intelligence algorithm, and deep learning to build the MS-SSA-LSTM model. Firstly, we crawl the East Money forum posts information to establish the unique sentiment dictionary and calculate the sentiment index. Then, the Sparrow Search Algorithm (SSA) optimizes the Long and Short-Term Memory network (LSTM) hyperparameters. Finally, the sentiment index and fundamental trading data are integrated, and LSTM is used to forecast stock prices in the future. Experiments demonstrate that the MS-SSA-LSTM model outperforms the others and has high universal applicability. Compared with standard LSTM, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of MS-SSA-LSTM is improved by 10.74% on average. We found that: 1) Adding the sentiment index can enhance the model’s predictive performance. 2) The LSTM’s hyperparameters are optimized using SSA, which objectively explains the model parameter settings and improves the prediction effect. 3) The high volatility of China’s financial market is more suitable for short-term prediction.

Generating a sentiment dictionary in R and dictionary-based sentiment analysis in Turkish texts

Abstract Dictionary-based sentiment analysis is a text mining application that allows comments about the sentimental states of the text or documents through the sentimental poles of the words. In recent years, it has become quite popular in many disciplines such as trade, health, education, usage for various purposes. It is applied in many languages depending on the sentiment dictionaries, which are the main component, are created. Unlike inflectional languages such as English and German, the number of dictionary-based sentiment analysis studies using the Turkish language, which is a sequencing language, is very limited. When the literature of sentiment analysis is examined separately in terms of the material used; it is seen that the number of studies on long-format literary texts, which have been functioning as sentiment transmission throughout history, is also quite limited. In the light of these knowledges, a comprehensive method of creating a domain-specific sentiment dictionary and applying dictionary-based sentiment analysis on Turkish texts is proposed in the R software.

АНАЛИЗ ТОНАЛЬНОСТИ ТЕКСТОВЫХ ОТЗЫВОВ С ПРИМЕНЕНИЕМ ТОНАЛЬНЫХ СЛОВАРЕЙ И КАРДИНАЛЬНОСТИ НЕЧЕТКОГО МНОЖЕСТВА

Sentiment or opinion analysis aims to determine the polarity of people's opinions in relationto any product, service, event or any person. One of the most common methods used in sentimentanalysis of text content is natural language processing. Sentiment analysis of natural languagetext can be assessed using numerous methodologies such as machine learning algorithms andstatistical tools, while the application of fuzzy logic is not common. The use of fuzzy logic waschosen for the following reasons. First, fuzzy logic handles linguistic uncertainty well. This way ofdefining the problem leads to a reduction in bias, both positively and negatively. Secondly, learning approaches based on fuzzy rules are fundamentally different from those learning approachesthat are widely used in sentiment classification, such as support vector machines, naive Bayes,etc., as they relate to generative learning, i.e. i.e. the goal of learning is to assess the degree towhich an instance belongs to each individual class. The proposed model for sentiment analysis oftext reviews is based on the use of tone lexicons using fuzzy logic and consists of four main stages.The steps include tokenization, word bag model formulation, sentiment fuzzy score formulation,and polarity assignment. In the proposed model, the power of the fuzzy set is used as a measure ofthe evaluation of the indicators of the polarity of words. Word polarity values are obtained byapplying two sentiment lexicons: SentiWordNet and AFINN. Two versions of the model were createddepending on the type of vocabulary used: based on SentiWordNet and AFINN. Comparisonof the presented approach based on fuzzy logic with other dictionary-based methods demonstratesthe superiority of the developed models based on the application of fuzzy logic.

고등학생 논설문 감성 표현과 글 수준의 상관관계 연구

Objectives In this study, the problem was that the current writing evaluation did not take into account the emotional level of student writing. In order to examine the correlation between the sentiment level of high school students' editorial text and the level of writing, an persuasive sentiment lexicon was prepared, the sentiment level was measured, and the correlation between the sentiment level and the evaluation score was analyzed. Methods A corpus was created by collecting 132 student essays on two subjects, and the emotional dictionary of the corpus was constructed by determining the emotional level of the vocabulary as the expert's rating. By measuring the emotional level with the editorial sentiment dictionary, the correlation with the writing evaluation score was examined, and specifically, it was confirmed whether there was a difference in the correlation according to the upper/lower level. Results In both the KNU sentiment dictionary and the editorial sentiment dictionary, the degree of emotion and the evaluation score were positively correlated, and the correlation degree of the editorial sentiment dictionary (0.543) was higher than the KNU sentiment dictionary (0.368). Although the degree of correlation was different for each topic, both the emotional level and the writing level showed a positive correlation, especially the number of positive and negative words and the highest correlation. As a result of examining the texts by dividing them into upper, middle and lower levels, the correlation between the lower level text and the emotional level was high (0.571), and the upper level had no significant correlation with the emotional level. Conclusions It was found that the higher the emotional level of the editorial text, the higher the writing level, and the lower-level writing had a higher correlation with the emotional level. It was confirmed that it is necessary to use emotional words appropriately in order to effectively convey one's argument to the other party, rather than exclude the degree of emotion from the editorial text. For students with low writing ability, since the amount of words has a great influence on their writing level, it is necessary to learn various words including emotional words.

A model fusion method based on multi-source heterogeneous data for stock trading signal prediction

In the prediction of turning points (TPs) of time series, the improved model of integrating piecewise linear representation and weighted support vector machine (IPLR-WSVM) has achieved good performance. However, due to the single data source and the limitation of algorithm, IPLR-WSVM has encountered challenges in profitability. In this paper, a model fusion method based on multi-source heterogeneous data and different learning algorithms is proposed for the prediction of TPs (MF-MSHD). Multi-source heterogeneous data include weighted unstructured and structured information with different granularities. RF, WSVM, BPNN, GBDT, and LSTM are selected to be the learning algorithms. The differences among meta-models are constructed by different inputs and algorithms as much as possible, and a model fusion rule is designed to determine the final TPs. Moreover, the TPs are generated based on the characteristics of individual stock. For sentiment analysis, a more accurate sentiment dictionary of stock market comments is established. Specifically, the fine-grained data is introduced to jointly determine the accurate trading moment. The prediction level of the proposal improves the accuracy and profitability, and also outperforms the composite indexes. Experimental results show that the profit rate of randomly selected stocks in MF-MSHD reaches 0.5172, while the highest value is 0.2841 in single meta-model and 0.0992 in buy and hold strategy, respectively. The other indicators including the accuracy are also modified. Compared with the increases of 0.1648, 0.4051, and 0.3397 in Shanghai Composite Index, Shenzhen Composite Index, and CSI 300 Index, MF-MSHD shows higher profitability in stock trading signal prediction.

Deep learning enabled cross‐lingual search with metaheuristic web based query optimization model for multi‐document summarization

SummaryDue to the exponential increase in the generation of digital documents and in the online search user diversity, multilingual information is highly available on the Internet. However, the huge amount of multilingual data cannot be analyzed manually. Therefore, cross lingual multi‐document summarization (CLMDS) model is introduced to generate a summary of several documents in which the summary language is different from the source document language. This paper presents a Deep Learning Enabled Cross‐lingual Search with Metaheuristic based Query Optimization (DLCLS‐MQO) model for Multi‐document summarization. The DLCLS‐MQO model allows to offer a query in Tamil, summarize several English documents, and lastly translate the summary into Tamil. The DLCLS‐MQO model encompasses four stages of operation such as multilingual search, query optimization, automatic sematic lexicon builder, and document summarization. Firstly, bidirectional long short‐term memory (BiLSTM) model is applied to perform multilingual searching process. Followed by, sunflower optimization (SFO) algorithm based query optimization process is carried out. Moreover, global vectors (GloVe) method is used for the construction of domain oriented sentiment lexicons. Finally, extreme gradient boosting (XGBoost) model is applied for the CLMDS. A detailed simulation analysis takes place to highlight the betterment of the DLCLS‐MQO model. The resultant experimental values portrayed the superior performance of the DLCLS‐MQO model over the compared methods.

A Lexicon Enhanced Collaborative Network for targeted financial sentiment analysis

The increasing interest around emotions in online texts creates the demand for financial sentiment analysis. Previous studies mainly focus on coarse-grained document-/sentence-level sentiment analysis, which ignores different sentiment polarities of various targets (e.g., company entities) in a sentence. To fill the gap, from a fine-grained target-level perspective, we propose a novel Lexicon Enhanced Collaborative Network (LECN) for targeted sentiment analysis (TSA) in financial texts. In general, the model designs a unified and collaborative framework that can capture the associations of targets and sentiment cues to enhance the overall performance of TSA. Moreover, the model dynamically incorporates sentiment lexicons to guide the sentiment classification, which cultivates the model faculty of understanding financial expressions. In addition, the model introduces a message selective-passing mechanism to adaptively control the information flow between two tasks, thereby improving the collaborative effects. To verify the effectiveness of LECN, we conduct experiments on four financial datasets, including SemEVAL2017 Task5 subset1, SemEVAL2017 Task5 subset2, FiQA 2018 Task1, and Financial PhraseBank. Results show that LECN achieves improvements over the state-of-art baseline by 1.66 p.p., 1.47 p.p., 1.94 p.p., and 1.88 p.p. in terms of F1-score. A series of further analyses also indicate that LECN has a better capacity for comprehending domain-specific expressions and can achieve the mutually beneficial effect between tasks.