Automated classification of text sentiment

The rapid development of information technology has changed the way people interact and express their opinions on public policies, including the People's Housing Savings (Tapera) policy in Indonesia. People now primarily express their views openly on social media platforms like Twitter, generating a substantial amount of text data for analysis to understand public sentiment. However, the main challenge in this sentiment analysis is determining the most effective machine learning algorithm for classifying public opinion with high accuracy. This study aims to compare the performance of three machine learning algorithms, namely Naïve Bayes, Support Vector Machine, and Random Forest, in analyzing public sentiment towards the Tapera policy. This study analyzes public comment data obtained from Twitter. We measure the accuracy of each algorithm to determine its optimal performance in sentiment classification. The research method consists of several stages, starting with data collection, text preprocessing to clean and prepare data, and then applying the three algorithms to analyze sentiment. The results showed that Naïve Bayes had the highest accuracy of 69.17%, followed by Support Vector Machine with an accuracy of 68.42%, and Random Forest with an accuracy of 66.17%. This shows that Naïve Bayes is the most effective algorithm to use in sentiment analysis of public comments related to the Tapera policy, especially in the context of complex text data from social media. The conclusion of this study is that Naïve Bayes is superior in classifying public sentiment towards the Tapera policy compared to Support Vector Machine and Random Forest. As a result, this study makes a significant contribution to selecting the most appropriate machine learning algorithm for public sentiment analysis towards public policy, which in turn can help the government understand and respond to public perceptions more effectively.

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

Sentiment analysis of Chinese micro-blog based on sentiment dictionary has become a challenging research subject in the field of artificial intelligence. However, due to insufficient sentiment words, Chinese micro-blog sentiment analysis is difficult to process high accuracy. Aimed at this issue, we propose a method for constructing multiple sentiment dictionaries, which mainly constructs original sentiment dictionary, emoji dictionary, and other related dictionaries. Among them, we have innovatively constructed a Chinese micro-blog new word sentiment dictionary. Multiple sentiment dictionaries increase the coverage of sentiment words. At the same time, we further analyze semantic rule sets between Chinese micro-blog texts and take the inter-sentence analysis rules and sentence pattern analysis rules into the sentiment analysis of Chinese micro-blog, which further improves the accuracy of Chinese micro-blog sentiment analysis. Finally, based on the method of multiple sentiment dictionaries and semantic rule sets, we propose an algorithm for Chinese micro-blog sentiment calculation from complex sentences to clauses and then from clauses to words, and finally combined with the emoji. This algorithm can accurately classify Chinese micro-blog into positive Chinese micro-blog, negative Chinese micro-blog, and neutral Chinese micro-blog. The experimental results show that this method has greatly improved the sentiment analysis of Chinese micro-blog.

The need to gather useful information efficiently from a vast amount of online data has given rise to the interdisciplinary field of automated sentiment analysis. In general, sentiment analysis researchers agree that detecting implicit sentiments is more challenging than recognizing explicit ones. While the latter can be recognized more easily through the presence of sentiment-laden words, the former are conveyed indirectly, usually by describing a situation that invokes or can be interpreted as related to a specific sentiment. Attempts to deal with the challenge of implicit sentiments have leveraged sentiment lexicons containing phrasal entries capable of capturing subtle clues for inferring sentiments. The present research aims to establish an automated lexicon construction process to build phrasal sentiment lexicons by extracting regular phrasal patterns from domain-specific text corpora. The key challenge in detecting such patterns is that most phrasal units have low frequencies in text corpora and occur in different lexical forms even when they convey similar semantics. For instance, in beauty product reviews, ‘sting my face’ and ‘burn my skin’ are lexically different phrases that describe the same negative experience. Because of the existence of many phrasal variants, each with a low frequency, it is challenging to detect them from text corpora. This issue is related to the common language phenomenon of word sparsity. To address this problem, the present research set out to investigate the plausibility of a linguistically-motivated approach that uses semantic frames in FrameNet for frame-based semantic abstraction. The key idea is that, by creating abstract semantic representations for texts, semantically similar phrasal units represented by the same frames will be grouped together, creating a more compact and less sparse textual space that facilitates the detection of regular phrasal patterns. Two prerequisites need to be in place for this frame-based abstraction approach to work properly. First, the frame identification problem must be dealt with adequately. Frame identification is a common subtask in frame-semantic parsing. It disambiguates a marked target in a sentence (a word or a phrase) and associates it with the most likely frame in FrameNet. In this research, a robust frame identification model called Positional Attention-based Frame Identification with BERT (PAFIBERT) was developed, which outperformed existing solutions and was proven robust in handling unseen targets (i.e., out-of-dictionary targets not listed in FrameNet). The second prerequisite for frame-based semantic abstraction is related to the degree of abstraction provided by FrameNet’s frames. Since FrameNet was not initially created for sentiment-related tasks, some information that is essential for distinguishing positive opinions from negative ones is not encoded in the frames. The present research incorporated an appraisal concept called graduation into FrameNet to provide discriminative sentiment-related information, resulting in a sentiment-aware version of FrameNet known as GradFrameNet. Using GradFrameNet and PAFIBERT, this research demonstrated that the frame-based abstraction approach did not cause substantial loss of information but rather generalized in a sentiment-aware manner to extract valuable phrasal clues for sentiment analysis. With the above two requirements fulfilled, the final stage of this research established a domain-specific sentiment lexicon construction process that applied frame-based semantic abstraction to increase the coverage of phrasal lexicons. From the performance gain obtained by incorporating the lexicons into sentiment classifiers, it was found that the integration of semantic abstraction was beneficial to the lexicon construction process despite the noise introduced by the imperfect frame identification step. This work makes several original contributions. First, it contributes to the existing knowledge of corpus-based lexicon construction by addressing the issue of lexicon coverage and dealing specifically with the difficulty of extracting phrasal patterns from sparse textual corpora using a novel frame-based abstraction approach. As one of the first efforts to examine the potential of FrameNet in a rather novel setting and an early attempt to realize the graduation phenomenon on a large scale, this research enhances the understanding of how semantic frames can be combined with the graduation concept to provide abstract yet semantic-rich and sentiment-aware text representations. On a practical level, GradFrameNet and PAFIBERT also benefit the community of researchers and practitioners as useful linguistic resources or tools for sentiment analysis. In addition, the findings in this research shed light on the potential of the frame-based abstraction approach in confronting the fundamental problem of word sparsity, which is an inherent characteristic of textual data that poses additional challenges for various text mining tasks. Finally, since frame identification is a pivotal subtask in frame-semantic parsing, this research also makes an important contribution to the field by providing a model that achieved new state-of-the-art results.

The existing methods for Chinese sentiment Labeling mainly relies on the artificial sentiment corpus, but a sentiment word in the corpus may not be sentiment words in different sentences. This paper proposes a new method to label the words in the sentences by combining deep convolution neural network with sequential algorithm., We first extract the aspects comprised by words vectors, part of speech vectors, dependent syntax vectors to train the deep convolution neural network, and then employ the sequential algorithm to obtain the sentiment annotation of the sentence. Experimental results verify that our method is effective for sentiment labeling. Considering that the identification of the implicit aspects can improve the completeness of sentiment analysis, we suggest to construct the tuples including aspect, sentiment shifter, sentiment intensity, sentiment words after obtaining the sentiment labels for each word in the sentence. We develop new algorithm for implicit aspect identification by taking the two key factors of the aspects as a topic and the match degree of aspects and sentiment words, and the human language habit. The experiment demonstrates that the algorithm can effectively identify the implicit aspect. In this paper, we solve the problem of sentiment labeling and implicit aspect recognition in sentiment analysis. As a new tool for sentiment analysis, our method can be applied to the enterprise management information analysis, such as product online review, product online reputation, brand image and consumer preference management, and can also be used for the sentiment analysis of large-scale text data.

The rise of social media such as blogs and social networks has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations, therefore many are now looking to the field of sentiment analysis. In this paper, we present a feature-based sentence level approach for Arabic sentiment analysis. Our approach is using Arabic idioms/saying phrases lexicon as a key importance for improving the detection of the sentiment polarity in Arabic sentences as well as a number of novels and rich set of linguistically motivated features contextual Intensifiers, contextual Shifter and negation handling), syntactic features for conflicting phrases which enhance the sentiment classification accuracy. Furthermore, we introduce an automatic expandable wide coverage polarity lexicon of Arabic sentiment words. The lexicon is built with gold-standard sentiment words as a seed which is manually collected and annotated and it expands and detects the sentiment orientation automatically of new sentiment words using synset aggregation technique and free online Arabic lexicons and thesauruses. Our data focus on modern standard Arabic (MSA) and Egyptian dialectal Arabic tweets and microblogs (hotel reservation, product reviews, etc.). The experimental results using our resources and techniques with SVM classifier indicate high performance levels, with accuracies of over 95%.

The popularity of mobile commerce has offered many new challenges for investigating public sentiments. With an uncountable number of stores and products available on the marketplace, customers heavily relied on the comments or reviews posted by others to support their buying decisions. For the online retailer's side, these text reviews are valuable resources to understand the latest customer expectations and devise a better product plan for launching suitable products to customers. Sentiment analysis is then developed for the evaluation of a significant amount of text data by searching the sentiment words. Nevertheless, different writers may have different perceptions on the sentiment words, and hence, this inconsistency would be amplified. In this connection, a novel approach to obtain public sentiment by combining the topic modeling, fuzzy set, and multi-criteria decision-making approaches is proposed. The uncertainty of different perceptions on the sentiment words is remedied by fuzzy-set.

Sentiment analysis has been widely used in text mining of social media to discover valuable information from user reviews. Sentiment lexicon is an essential tool for sentiment analysis. Recent research studies indicate that constructing sentiment lexicons for special domains can achieve better results in sentiment analysis. However, it is not easy to construct a sentiment lexicon for a specific domain because most current methods highly depend on general sentiment lexicons and complex linguistic rules. In this paper, the construction of sentiment lexicon is transformed into a training‐optimization process. In our scheme, the accuracy of sentiment classification is used as the optimization objective. The candidate sentiment lexicons are regarded as the individuals that need to be optimized. Then, two genetic algorithms are specially designed to adjust the values of sentiment words in lexicon. Finally, the best individual evolved in the presented genetic algorithms is selected as the sentiment lexicon. Our method only depends on some labelled texts and does not need any linguistic knowledge or prior knowledge. It provides a simple and easy way to construct a sentiment lexicon in a specific domain. Experiment results show that the proposed method has good flexibility and can generate high‐quality sentiment lexicon in specific domains.

Sentiment analysis also referred to as opinion mining, plays a significant role in automating the identification of negative, positive, or neutral sentiments expressed in textual data. The proliferation of social networks, review sites, and blogs has rendered these platforms valuable resources for mining opinions. Sentiment analysis finds applications in various domains and languages, including English and Arabic. However, Arabic presents unique challenges due to its complex morphology characterized by inflectional and derivation patterns. To effectively analyze sentiment in Arabic text, sentiment analysis techniques must account for this intricacy. This paper proposes a model designed using the transformer model and deep learning (DL) techniques. The word embedding is represented by Transformer-based Model for Arabic Language Understanding (ArabBert), and then passed to the AraBERT model. The output of AraBERT is subsequently fed into a Long Short-Term Memory (LSTM) model, followed by feedforward neural networks and an output layer. AraBERT is used to capture rich contextual information and LSTM to enhance sequence modeling and retain long-term dependencies within the text data. We compared the proposed model with machine learning (ML) algorithms and DL algorithms, as well as different vectorization techniques: term frequency-inverse document frequency (TF-IDF), ArabBert, Continuous Bag-of-Words (CBOW), and skipGrams using four Arabic benchmark datasets. Through extensive experimentation and evaluation of Arabic sentiment analysis datasets, we showcase the effectiveness of our approach. The results underscore significant improvements in sentiment analysis accuracy, highlighting the potential of leveraging transformer models for Arabic Sentiment Analysis. The outcomes of this research contribute to advancing Arabic sentiment analysis, enabling more accurate and reliable sentiment analysis in Arabic text. The findings reveal that the proposed framework exhibits exceptional performance in sentiment classification, achieving an impressive accuracy rate of over 97%.

This study evaluates Indonesian public perceptions of the application of Pancasila Legal Philosophy in international diplomacy through sentiment analysis on Twitter. Using text mining and machine learning algorithms—Naïve Bayes, Support Vector Machine (SVM), and Random Forest—1,000 tweets containing keywords such as “Pancasila diplomacy,” “Indonesia at the UN,” and “Indonesian foreign policy” were collected and classified into positive, negative, and neutral categories. The distribution of sentiment indicates that 60% of tweets express positive perceptions, highlighting pride in Indonesia’s promotion of Pancasila values in global forums, 25% remain neutral, and 15% are negative, reflecting criticism of perceived inconsistencies between Pancasila and diplomatic practice. Model evaluation employed a confusion matrix and metrics of accuracy, precision, and recall across sentiment classes. Results demonstrate that Random Forest outperformed other models with 91% accuracy, stable precision, and recall across all classes. By comparison, SVM achieved 89% accuracy with consistent performance in high-dimensional text data, while Naïve Bayes recorded 85% accuracy but was less effective in handling class imbalance, particularly in neutral–negative distinctions. The Random Forest model explained the greatest variance in sentiment classification, confirming its strength in processing short and contextually complex texts such as tweets. Practically, these findings provide a foundation for developing a real-time sentiment monitoring system to support adaptive and participatory diplomacy. Integrating sentiment analysis into policy design enables the Ministry of Foreign Affairs to anticipate public responses, strengthen diplomacy narratives rooted in Pancasila values, and build a data-driven ecosystem for public diplomacy. This contributes to inclusive, ethical, and responsive foreign policy aligned with Indonesia’s state philosophy.

Spectator periodicals contributed to spreading the ideas of the Age of Enlightenment, a turning point in human history and the foundation of our modern societies. In this work, we study the spirit and atmosphere captured in the spectator periodicals about important social issues from the 18th century by analyzing text sentiment of those periodicals. Specifically, based on a manually annotated corpus of over 3 700 issues published in five different languages and over a period of more than one hundred years, we conduct a three-fold sentiment analysis: First, we analyze the development of sentiment over time as well as the influence of topics and narrative forms on sentiment. Second, we construct sentiment networks to assess the polarity of perceptions between different entities, including periodicals, places and people. Third, we construct and analyze sentiment word networks to determine topological differences between words with positive and negative polarity allowing us to make conclusions on how sentiment was expressed in spectator periodicals.Our results depict a mildly positive tone in spectator periodicals underlining the positive attitude towards important topics of the Age of Enlightenment, but also signaling stylistic devices to disguise critique in order to avoid censorship. We also observe strong regional variation in sentiment, indicating cultural and historic differences between countries. For example, while Italy perceived other European countries as positive role models, French periodicals were frequently more critical towards other European countries. Finally, our topological analysis depicts a weak overrepresentation of positive sentiment words corroborating our findings about a general mildly positive tone in spectator periodicals.We believe that our work based on the combination of the sentiment analysis of spectator periodicals and the extensive knowledge available from literary studies sheds interesting new light on these publications. Furthermore, we demonstrate the inclusion of sentiment analysis as another useful method in the digital humanist’s distant reading toolbox.

Sentiment analysis is an active research area in today's era due to the abundance of opinionated data present on online social networks. Semantic detection is a sub-category of sentiment analysis which deals with the identification of sentiment orientation in any text. Many sentiment applications rely on lexicons to supply features to a model. Various machine learning algorithms and sentiment lexicons have been proposed in research in order to improve sentiment categorization. Supervised machine learning algorithms and domain specific sentiment lexicons generally perform better as compared to the unsupervised or semi-supervised domain independent lexicon based approaches. The core hindrance in the application of supervised algorithms or domain specific sentiment lexicons is the unavailability of sentiment labeled training datasets for every domain. On the other hand, the performance of algorithms based on general purpose sentiment lexicons needs improvement. This research is focused on building a general purpose sentiment lexicon in a semi-supervised manner. The proposed lexicon defines word semantics based on Expected Likelihood Estimate Smoothed Odds Ratio that are then incorporated with supervised machine learning based model selection approach. A comprehensive performance comparison verifies the superiority of our proposed approach.

Stock market trends can be affected by external factors such as public sentiment and political events. The goal of this research is to find whether or not public sentiment and political situation on a given day can affect stock market trends of individual companies or the overall market. For this purpose, the sentiment and situation features are used in a machine learning model to find the effect of public sentiment and political situation on the prediction accuracy of algorithms for 7 days in future. Besides, interdependencies among companies and stock markets are also studied. For the sake of experimentation, stock market historical data are downloaded from Yahoo! Finance and public sentiments are obtained from Twitter. Important political events data of Pakistan are crawled from Wikipedia. The raw text data are then pre-processed, and the sentiment and situation features are generated to create the final data sets. Ten machine learning algorithms are applied to the final data sets to predict the stock market future trend. The experimental results show that the sentiment feature improves the prediction accuracy of machine learning algorithms by 0–3%, and political situation feature improves the prediction accuracy of algorithms by about 20%. Furthermore, the sentiment attribute is most effective on day 7, while the political situation attribute is most effective on day 5. SMO algorithm is found to show the best performance, while ASC and Bagging show poor performance. The interdependency results indicate that stock markets in the same industry show a medium positive correlation with each other.

As the social applications are gaining the more popularity, different kinds of social media platform are ready to publish and express the emotions in the form of reviews. In which, detecting the concealed sentiment patterns and discovering knowledge in the huge user-generated inputs is a challenging task and has greater social significance value. In traditional sentiment analysis approaches, statistical correlation between words is considered. Whereas, dependency factor between aspects and the sentiment words are ignored, which has a greater impact factor on overall sentiment analysis. In this paper, we propose a new supervised topic level sentiment model (SSM), which is capable of handling overall sentiment analysis problems. Belief maximisation algorithm is used in SSM model, and Dirichlet distribution is used to estimate aspects and sentiment words. In order to prepare and modernise new documents, a hyperparameter Gibbs sampling method is used. We conducted experiments on reviews related to different products in multiple documents and the results state that the SSM model outperforms the on-hand algorithm in terms of aspect recognition and overall sentiment prediction accuracy.

As the social applications are gaining the more popularity, different kinds of social media platform are ready to publish and express the emotions in the form of reviews. In which, detecting the concealed sentiment patterns and discovering knowledge in the huge user-generated inputs is a challenging task and has greater social significance value. In traditional sentiment analysis approaches, statistical correlation between words is considered. Whereas, dependency factor between aspects and the sentiment words are ignored, which has a greater impact factor on overall sentiment analysis. In this paper, we propose a new supervised topic level sentiment model (SSM), which is capable of handling overall sentiment analysis problems. Belief maximisation algorithm is used in SSM model, and Dirichlet distribution is used to estimate aspects and sentiment words. In order to prepare and modernise new documents, a hyperparameter Gibbs sampling method is used. We conducted experiments on reviews related to different products in multiple documents and the results state that the SSM model outperforms the on-hand algorithm in terms of aspect recognition and overall sentiment prediction accuracy.