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

Word embedding vectorization is more efficient than Bag-of-Word in word vector size. Word embedding also overcomes the loss of information related to sentence context, word order, and semantic relationships between words in sentences. Several kinds of Word Embedding are often considered for sentiment analysis, such as Word2Vec and FastText. Fast Text works on N-Gram, while Word2Vec is based on the word. This research aims to compare the accuracy of the sentiment analysis model using Word2Vec and FastText. Both models are tested in the sentiment analysis of Indonesian hotel reviews using the dataset from TripAdvisor.Word2Vec and FastText use the Skip-gram model. Both methods use the same parameters: number of features, minimum word count, number of parallel threads, and the context window size. Those vectorizers are combined by ensemble learning: Random Forest, Extra Tree, and AdaBoost. The Decision Tree is used as a baseline for measuring the performance of both models. The results showed that both FastText and Word2Vec well-to-do increase accuracy on Random Forest and Extra Tree. FastText reached higher accuracy than Word2Vec when using Extra Tree and Random Forest as classifiers. FastText leverage accuracy 8% (baseline: Decision Tree 85%), it is proofed by the accuracy of 93%, with 100 estimators.

BackgroundLoneliness is a global public health issue affecting a considerable number of people as well as burdening the public health system and increasing the risk of other life-threatening and life-damaging conditions. In USA an estimated 17% adults aged 18–70 report loneliness. The monetary loss as result of loneliness is estimated to be between USD 8074.80 and USD 12,0777.70 per person per year in the United Kingdom. But the dynamics of loneliness are not understood. Social media platforms have become a valuable source of data to study this phenomenon. ObjectivesThis paper aims to visualize the frequency of loneliness-related themes and topics in Twitter data. By using natural language (NLP) processing, sentiment analysis, and topic modeling, we seek to understand prevalent sentiments and concerns. Through interactive tree maps and radar plots, we present an engaging view of loneliness dimensions, allowing users to explore and gain insights into this issue on social media. We focus on comparative analysis of USA and India through analyzing tweets from both countries on loneliness. These two countries are the biggest countries population-wise where access to Twitter is legally allowed. MethodsThis study consists of two parts. In the first part, we employ NLP techniques and machine learning algorithms to extract and analyze tweets containing keywords related to loneliness. Through sentiment analysis and topic modeling, we discern linguistic patterns and contextual information to categorize the recurring themes and topics. Advanced text analytics is used to gain nuanced insights into the experiences, emotions, and challenges connected with loneliness. In the second part, interactive visualizations are developed to present the findings in an engaging and intuitive manner. Techniques such as tree maps and radar plots are utilized to transform the analyzed data into visually appealing representations. ResultsThe analysis of Twitter data yields valuable knowledge about the prevalence and nature of themes and topics associated with loneliness. The interactive visualizations present a comprehensive view of the sentiments and concerns expressed by Twitter users. These interactive plots provide a holistic view of the distribution of themes and topics associated with loneliness, allowing experts to explore and interact with the data, gaining deeper insights into the complexities surrounding this issue. ConclusionThis paper successfully explores themes and topics related to loneliness on Twitter by employing NLP, sentiment analysis, and topic modeling. The interactive visualizations enhance the accessibility and usability of the findings, providing valuable insights for various stakeholders. The study contributes to a deeper comprehension of loneliness in the context of social media.

The use of online consumer reviews (OCRs) websites like Yelp or TripAdvisor has increasingly gained popularity over the years. However, many products or services now have a large number of online reviews, which makes it difficult for consumers to decide which reviews to pay attention to, or for businesses to identify which critical areas to improve on. The use of text mining and sentiment mining techniques is deemed important to automatically process the content of online reviews and help improve review usefulness. Applying a four-phase research model, our study demonstrated data extraction and cleaning, topic modeling using Latent Dirichlet allocation (LDA) to extract five topics (Price, Time, Food, Service, and Location), sentiment analysis using Python TextBlob to aggregate consumer sentiment per topic from a Yelp restaurant reviews dataset, and model performance evaluation. We proposed that the design of recommender systems for OCRs or business decision-making systems can be faster, simpler, and more useful by integrating automatic topics extraction and sentiment analysis.

AirAsia has emerged as a dominant force among prominent low-cost airlines in recent years. However, the COVID-19 pandemic outbreak has severely impacted airline services, including AirAsia. There is a strong need for airline services to monitor customer experience and satisfaction from online customer reviews on the website to keep pace with changing customer perceptions toward their service quality. A growing number of travelers choose to express their experiences and emotions on online customer review platforms, resulting in substantial online airline service evaluations. The research analyzes 796 online customer reviews from Skytrax, a well-known online airline review website. The information hidden in customer-generated reviews is analyzed with the text mining technique, including topic modeling and sentiment analysis. The research uses the Latent Dirichlet Allocation (LDA) model for topic analysis and the Valence Aware Dictionary for Sentiment Reasoning (VADER) model for sentiment analysis. The sentiment ratio for AirAsia’s online reviews is approximately 59% positive and 41% negative. Only four reviews are neutral. The findings indicate that the online review of AirAsia has a greater proportion of positive sentiments than negative sentiments. In addition, the topic modeling shows hidden topics with the top high-probability keywords concerned with interior and seat, baggage, online service, staff service, flight schedule, and refund. The research demonstrates using sentiment analysis and topic modeling on customer review data as a more thorough alternative to survey-based models for researching airline service. The research contributes to the methodological advancements in text mining analysis and expands the current knowledge of customer review data.

Sentiment analysis refers to the automated process of identifying the underlying emotional tone conveyed in a text. This task has become increasingly crucial today due to the exponential growth of opinion-oriented text generation on various online platforms including social media and e-commerce websites. The process of sentiment analysis has evolved through different phases like the use of handcrafted rules, sentiment-oriented dictionaries, machine learning, deep learning, transformer models, and large language models. These different phases have enriched the field of sentiment analysis and enabled it to get better insights into text for sentiment analysis. One of the most impactful developments in sentiment analysis was the creation of embedding’s for words or sentences, which are useful for representing unstructured data as structured data. These embedding are representing words or sentences as numerical vectors in a high-dimensional space and have proven effective in capturing the semantic relationships between words. By leveraging the power of embedding’s, sentiment analysis models can understand the complexities of human language and provide more precise insights into people's emotions and opinions. However, word embedding’s not inherently generated for sentiment analysis, which means that they are not inherently sentiment-oriented. Therefore, many researchers have focused on developing sentiment-oriented word embedding’s for sentiment words, but they have overlooked the importance of intensity words such as “little”, “high”, and “extremely”. These intensity words determine the level of sentiment expressed in text, which is particularly useful for fine-level sentiment analysis. To address this issue, a review proposes an intensity-aware word embedding development model and sentiment analysis with ML.

Hotel reviews are an important driving factor for hotel business. They can benefit guests to make informed hotel selections, and hotels to tackle their deficiencies and better their performance. In this paper, we propose an opinion mining approach that is applied to hotel reviews. The approach combines both lexical and word vectors’ methods to classify a review. An opinion-oriented sphere of word vectors is constructed, where words correspond to vectors on the sphere. The locations of the vectors reflect the similarity relations among the corresponding words, and they are derived using a simple relaxation algorithm. The classification task requires only a small-sized lexicon of positive and negative seeds. To measure the performance of our approach, we used a data set of hotel reviews. Moreover, we have conducted a comparative analysis using word vectors from other models. The results obtained proves the efficiency of our approach, giving better results at a significant reduction in computation time.

Many of today's online news websites and aggregator apps have enabled users to publish their opinions without respect to time and place. Existing works on topic-based sentiment analysis of product reviews cannot be applied to online news directly because of the following two reasons: 1 The dynamic nature of news streams require the topic and sentiment analysis model also to be dynamically updated. 2 The user interactions among news comments can easily lead to inaccurate topic and sentiment extraction. In this paper, we propose a novel probabilistic generative model DTSA to extract topics and the specified sentiments from news streams and analyze their evolution over time simultaneously. DTSA incorporates a multiple timescale model into a generative topic model. Additionally, we further consider the links among news comments to avoid the error caused by user interactions. Finally, we derive distributed online inference procedures to update the model with newly arrived data and show the effectiveness of our proposed model on real-world data sets.

Deep learning-based application for multilevel sentiment analysis of Indonesian hotel reviews

Although automatic sentiment analysis has been widely studied in the past decade, multilingualism remains an issue impairing real-life applications. This paper describes the development and evaluation of XLM-RLnews-8, a model based on XLM-RoBERTa-Large, domain-adapted using a novel dataset of multilingual news articles and fine-tuned for the tripartite sentiment analysis task. In addition, it provides a quantitative analysis of the Unified Multilingual Sentiment Analysis Benchmark, the dataset used for the fine-tuning and the in-domain evaluation. The model is also out-of-domain evaluated, on the IMDb dataset and a new multilingual news headlines silver dataset, and its performance is compared with current State-of-the-Art multilingual models. Models, notebooks and datasets developed for this publication are available at https://github.com/ElisaDiNuovo/XLM-RLnews-8/.

Learning the inherent meaning of a word in Natural Language Processing (NLP) has motivated researchers to represent a word at various levels of abstraction, namely character-level, morpheme-level, and subword-level vector representations. Syllable-Aware Word Embedding (SAWE) can effectively handle agglutinative and fusion-based NLP tasks. However, research attempts on assessing the SAWE on such extrinsic NLP tasks has been scanty, especially for low-resource languages in the context of code-mixing with English. A model to learn SAWE to extract semantics at fine-grained subunits of a word is proposed in this article, and the representative ability of the embeddings is assessed through sentiment analysis of code-mixed Telugu-English review corpora. Multilingual societies and advancements in communication technologies have accounted for the prolific usage of mixed data, which renders the State-of-the-Art (SOTA) sentiment analysis models developed based on monolingual data ineffective. Social media users in the Indian subcontinent exhibit a tendency to mix English and their respective native language (using the phonetic form of English) in expressing their opinions or sentiments. A code-mixing scenario provides flexibility to borrow words from a foreign language, usage of shorthand notations, elongation of vowels, and usage of words without following syntactic/grammatical rules, which renders the sentiment analysis of code-mixed data challenging to perform. Deep neural architectures like Long Short-Term Memory and Gated Recurrent Unit networks have been shown to be effective in solving several NLP tasks, such as sequence labeling, named entity recognition, and machine translation. In this article, a framework to perform sentiment analysis on a code-mixed Telugu-English review corpus is implemented. Both word embedding and SAWE are input to a unified deep neural network that contains a two-level Bidirectional Long Short-Term Memory/Gated Recurrent Unit network with Softmax as the output layer. The proposed model leverages the advantages of both word embedding and SAWE, which enable the proposed model to outperform existing SOTA code-mixed sentiment analysis models on a Telugu-English code-mixed dataset of the International Institute of Information Technology–Hyderabad and a dataset curated by the authors. The improvement realized by the proposed model on these datasets is [3% increase in F1-score and 2% increase in accuracy] and [7% increase in F1-score and 5% in accuracy], respectively, in comparison with the best-performing SOTA model.

Social media has been a real-world sensor to observe the pulse of society. Although it provides unique communication opportunities, it also brings along vital challenges. One of them is hate speech, which attacks a single individual or targeted groups. Previous researchers claim that among social networks, the Twitter platform is mostly used to spread hate speech. However, data on a larger scale makes it hard to capture and understand the nature of hate speech on Twitter within specific food brands. In this study, sentiment analysis techniques were used to filter hate speech on Twitter and on three popular food brands in Malaysia. This study was conducted in five phases, namely raw data collection, pre-processing, sentiment analysis, visualization, and performance evaluation. This corpus consists of 28,898 data samples based on user tweet searches. A Twitter API was created and SQLite was used to store all the sample data. VADER sentiment analysis is used to classify tweet sentiment into positive, negative, and neutral. In the visualization phase, these three food brands are visualized using a histogram to gain sentiment analysis insights. Then, three machine learning methods were implemented to predict the best model for sentiment analysis. The Decision Tree classifier outperforms the average accuracy in Support Vector Machine and Logistic Regression with 99.99% for the training data set. This study provides insights to assist humans in making decisions. With the growth of opinions expressed in multimedia on social media, such as spoken feedback on Twitter, sentiment analysis has the potential to become a more news aggregation and low-cost endeavour.

The purpose of this study is to understand public awareness of cybersecurity-related issues and discussions during the COVID-19 pandemic. Employees and confidential organizational data are vulnerable to cyber-attacks as a result of the pandemic, which has raised concerns about cyber security about the new normal of working from home. The public’s main sentiments and aspects related to cyber security concerns have been mined from tweets on the microblogging social media website Twitter. Sentiment Analysis and Topic Modeling techniques have been applied to understand the perspectives, emotions, and themes discussed by people. The analysis reveals people are becoming more aware of cybercrime-related concerns and are more positive than negative in combating the challenge of cybercrime. The paper also highlights the main themes revealed by Topic Modeling.

Online mode of education has been identified as the subtle solution to continue learning during the pandemic. However, the accessibility to online platforms, suitable devices, and connections are not equal across the globe thus raising the question of whether the opinion of the public in the South Asian region where the technology is not comparatively higher as in the western world would be the same as that to the global perspective. This study involves the sentiment analysis of natural language processing on recently tweeted data and concludes that the sentiment of the South Asian public remains positive as online education is the most suitable approach to overcome the learning difficulties during a pandemic. The study performs a ternary classification based on the polarity scores obtained from two robust lexicon-based sentiment analyzer tools namely VADER and TextBlob and observes that 63.2% of the tweets were positive, 30.5% of the tweets were neutral and around 6.3% of them were negative. Finally, topic modeling was also performed using the Latent Dirichlet Allocation method to gain insight into each of the classes.

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

Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon