Concept-level Sentiment Analysis Research Articles

A hybrid dependency-based approach for Urdu sentiment analysis

In the digital age, social media has emerged as a significant platform, generating a vast amount of raw data daily. This data reflects the opinions of individuals from diverse backgrounds, races, cultures, and age groups, spanning a wide range of topics. Businesses can leverage this data to extract valuable insights, improve their services, and effectively reach a broader audience based on users’ expressed opinions on social media platforms. To harness the potential of this extensive and unstructured data, a deep understanding of Natural Language Processing (NLP) is crucial. Existing approaches for sentiment analysis (SA) often rely on word co-occurrence frequencies, which prove inefficient in practical scenarios. Identifying this research gap, this paper presents a framework for concept-level sentiment analysis, aiming to enhance the accuracy of sentiment analysis (SA). A comprehensive Urdu language dataset was constructed by collecting data from YouTube, consisting of various talks and reviews on topics such as movies, politics, and commercial products. The dataset was further enriched by incorporating language rules and Deep Neural Networks (DNN) to optimize polarity detection. For sentiment analysis, the proposed framework employs predefined rules to trigger sentiment flow from words to concepts, leveraging the dependency relations among different words in a sentence based on Urdu language grammatical rules. In cases where predefined patterns are not triggered, the framework seamlessly switches to its sub-symbolic counterpart, passing the data to the DNN for sentence classification. Experimental results demonstrate that the proposed framework surpasses state-of-the-art approaches, including LSTM, CNN, SVM, LR, and MLP, achieving an improvement of 6–7% on Urdu dataset. In conclusion, this research paper introduces a novel framework for concept-level sentiment analysis of Urdu language data sourced from social media platforms. By combining language rules and DNN, the proposed framework demonstrates superior performance compared to existing methodologies, showcasing its effectiveness in accurately analyzing sentiment in Urdu text data.

CLSA-CapsNet: Dependency based concept level sentiment analysis for text

The refining of information from the immense amount of unstructured data on the internet can be a critical issue in identifying public opinion. It is difficult to extract relevant concepts from huge amounts of data. Concept level semantic parsing is improved over word-based investigation as it conserves the semantical data relevant to many-word articulations. The semantic proposals offer a superior comprehension of textual data and serve to altogether precision the exactness of numerous mining operations in text assignments. The extraction of concepts from textual data is a significant step forward in content analysis at the concept stage. We present a CLSA-CapsNet method that extracts concepts from natural language text. Then the extracted concepts are applied in Capsule networks (CapsNet). Moreover, the integration of Concept Level Sentiment Analysis (CLSA) and Capsule Networks (CapsNet) has not yet been implemented on the hotel review dataset. This is the first attempt, which we researched and embraced by the capsule network, to develop classification models for hotel reviews. The developed results demonstrated excellent performance with a prediction accuracy of 86.6% for CLSA-CapsNet models, respectively. Various similarities have also been made across our techniques and they are implemented by some other deep learning algorithms, such as rnn-lstm. Overall, the outstanding success obtained by CLSA-CapsNet in this investigation highlights its ability in the hotel review dataset. We likewise show exploratory outcomes, in which the proposed system outpaced the state-of-the-art CLSA-CapsNet model.

Enhanced concept-level sentiment analysis system with expanded ontological relations for efficient classification of user reviews

Background/introductionConcept-level sentiment analysis deals with the extraction and classification of concepts and features from user reviews expressed online about products and other entities like political leaders, government policies, and others. The prior studies on concept-level sentiment analysis have used a limited set of linguistic rules for extracting concepts and their associated features. Furthermore, the ontological relations used in the early works for performing concept-level sentiment analysis need enhancement in terms of the extended set of features concepts and ontological relations. MethodsThis work aims at addressing the aforementioned issues and tries to bridge the literature gap by proposing an extended set of linguistic rules for concept-feature pair extraction along with enhanced set ontological relations. Additionally, a supervised a machine learning technique is implemented for performing concept-level sentiment analysis. Results and conclusionsExperimental results depict the effectiveness of the proposed system in terms of improved efficiency (P: 88%, R: 88%, F-score: 88%, and A: 87.5%).

Listening to the patient voice: using a sentic computing model to evaluate physicians’ healthcare service quality for strategic planning in hospitals

Online physician reviews (OPRs), also known as electronic word of mouth, have become the primary source of information for patients while making health consultation decisions. However, different techniques to analyze these reviews by machines have not been frequently applied yet in this domain. In this study, a novel method for opinion mining is being proposed to fill the existing research gap, that is, a hybrid approach to sentic computing. This approach integrates artificial intelligence and semantic web techniques to implicitly analyze OPRs in order to evaluate patient perceptions of healthcare service quality. We develop our methodology by using the following three main tasks: (1) sentence-level topic spotting (a topic-analysis procedure) to extract major topics, (2) a sentic computing framework to perform concept-level sentiment analysis (polarity detection on the categorized sentences), and (3) root cause and strengths, weaknesses, opportunities, and threats (SWOT) analyses to identify SWOT for healthcare organizations. Analyses results of 47,499 OPRs from the UK-based website (Iwantgreatcare.org) show that the proposed hybrid approach has accurately classified concept words to their corresponding topics, and it has also outperformed the similar other methods of topic extraction in the healthcare domain. The results also indicate that the proposed approach can better contribute to the overall performance analyses for healthcare organizations, which could help practitioners in improving service-quality measures based on the real voices of patients. The proposed model also provides a theoretical basis for formulating quality measures for the healthcare sector.

A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks

Social media hold valuable, vast and unstructured information on public opinion that can be utilized to improve products and services. The automatic analysis of such data, however, requires a deep understanding of natural language. Current sentiment analysis approaches are mainly based on word co-occurrence frequencies, which are inadequate in most practical cases. In this work, we propose a novel hybrid framework for concept-level sentiment analysis in Persian language, that integrates linguistic rules and deep learning to optimize polarity detection. When a pattern is triggered, the framework allows sentiments to flow from words to concepts based on symbolic dependency relations. When no pattern is triggered, the framework switches to its subsymbolic counterpart and leverages deep neural networks (DNN) to perform the classification. The proposed framework outperforms state-of-the-art approaches (including support vector machine, and logistic regression) and DNN classifiers (long short-term memory, and Convolutional Neural Networks) with a margin of 10–15% and 3–4% respectively, using benchmark Persian product and hotel reviews corpora.

Finding “just right” books for children: analyzing sentiments in online book reviews

PurposeThis study is part of a larger research project which aims to investigate whether sentiments in online reviews on children’s books would represent significant factors which are useful for selecting the right books for children. This paper aims to examine whether positive, negative or neutral attitude would be directly associated with the overall ratings of books.Design/methodology/approachThe study investigates subjectivity and polarity of online reviews on children’s books such as neutral, positive or negative sentiment. For the investigation of a statistical association between the sentiment values and the rating scores, this study performs correlation analysis. For a clear explanation of the factors affecting the relationships between the sentiment value and the rating score, this study uses the concept-level sentiment analysis of online reviews.FindingsThe findings of this study demonstrate that there is a weak or low correlation between the sentiment value and the rating score of a book and they are hardly related for most books. The results of this study also uncover key contributing factors that affected the correlations between two variables and made the relationship weak.Research limitations/implicationsThis study increases awareness of the implications of online reviews as user-generated contents for complementing the existing controlled vocabulary.Practical implicationsThis study contributes to improving library catalogs by using latent topics extracted from online reviews which provide additional access points for assisting in the selection of books.Originality/valueAlthough several studies have conducted on online reviews in the domain of business, no research appears to exist on the sentiment analysis of online reviews about children’s books. This study attempts to address the potential and challenges associated with using online reviews to help find the right books for children.

Fuzzy formal concept analysis based opinion mining for CRM in financial services

Owing to the easy access to social media, consumers or customers are increasingly turning to social media to express their grievances and feedback on various products and services offered by the Banking, Financial, Services and Insurance industry. Because non-redressal of complaints eventually leads to customer churn, there is an urgent need to analyze the complaints. In this regard, we propose a novel descriptive analytics model that performs complaints/grievances analytics and summarizes the lengthy and verbose complaints concisely in a form that resembles association rules. The proposed hybrid model comprises fuzzy formal concept analysis and concept-level sentiment analysis (FFCA+SA) in tandem, which in turn is compared against formal concept analysis and concept-level sentiment analysis (FCA+SA). Because of the immediate fallout of the negative sentiments, a financial company is interested in studying them in more detail than the positive ones. Therefore, the model generates a list of ‘association rules’, the corresponding negative sentiment score along with the list of associated documents. Association rules are rank ordered according to the negative sentiment score, which in turn reflects severity affected services/products. The proposed model also provides interactive visualization that enables business analysts and managers to access a specific set of complaints without having to go through the entire set thoroughly. This saves a lot of time that would have otherwise been spent on cumbersome manual operations. Moreover, partial evaluation of the proposed methodology by human annotators yielded 64.06% matching score in terms of the opinions determination of aspects.

A Review of Sentiment Analysis Research in Chinese Language

Research on sentiment analysis in English language has undergone major developments in recent years. Chinese sentiment analysis research, however, has not evolved significantly despite the exponential growth of Chinese e-business and e-markets. This review paper aims to study past, present, and future of Chinese sentiment analysis from both monolingual and multilingual perspectives. The constructions of sentiment corpora and lexica are first introduced and summarized. Following, a survey of monolingual sentiment classification in Chinese via three different classification frameworks is conducted. Finally, sentiment classification based on the multilingual approach is introduced. After an overview of the literature, we propose that a more human-like (cognitive) representation of Chinese concepts and their inter-connections could overcome the scarceness of available resources and, hence, improve the state of the art. With the increasing expansion of Chinese language on the Web, sentiment analysis in Chinese is becoming an increasingly important research field. Concept-level sentiment analysis, in particular, is an exciting yet challenging direction for such research field which holds great promise for the future.

Leveraging multimodal information for event summarization and concept-level sentiment analysis

The rapid growth in the amount of user-generated content (UGCs) online necessitates for social media companies to automatically extract knowledge structures (concepts) from photos and videos to provide diverse multimedia-related services. However, real-world photos and videos are complex and noisy, and extracting semantics and sentics from the multimedia content alone is a very difficult task because suitable concepts may be exhibited in different representations. Hence, it is desirable to analyze UGCs from multiple modalities for a better understanding. To this end, we first present the EventBuilder system that deals with semantics understanding and automatically generates a multimedia summary for a given event in real-time by leveraging different social media such as Wikipedia and Flickr. Subsequently, we present the EventSensor system that aims to address sentics understanding and produces a multimedia summary for a given mood. It extracts concepts and mood tags from visual content and textual metadata of UGCs, and exploits them in supporting several significant multimedia-related services such as a musical multimedia summary. Moreover, EventSensor supports sentics-based event summarization by leveraging EventBuilder as its semantics engine component. Experimental results confirm that both EventBuilder and EventSensor outperform their baselines and efficiently summarize knowledge structures on the YFCC100M dataset.

Unsupervised Commonsense Knowledge Enrichment for Domain-Specific Sentiment Analysis

Sentiment analysis in natural language text is a challenging task involving a deep understanding of both syntax and semantics. Leveraging the polarity of multiword expressions—or concepts—rather than single words can mitigate the difficulty of such a task as these expressions carry more contextual information than isolated words. Such contextual information is the key to understanding both the syntactic and semantic structure of natural language text and hence is useful in tasks such as sentiment analysis. In this work, we propose a new method to enrich SenticNet (a publicly available knowledge base for concept-level sentiment analysis) with domain-level concepts composed of aspects and sentiment word pairs, along with a measure of their polarity. We process a set of unlabeled texts and, by considering the statistical co-occurrence information, generate a direct acyclic graph (DAG) of concepts. The polarity score of known concepts is propagated and used to compute polarity scores of new concepts. By designing and implementing our exhaustive algorithm, we are able to use a seed set containing only two sentiment words (good and bad). In our evaluation conducted on a dataset of hotel reviews, SenticNet was enriched by a factor of three (from 30,000 to nearly 90,000 concepts). The experiments demonstrate the merit of the concepts discovered by our method at improving sentence-level and aspect-level sentiment analysis tasks. Results of the two-factor ANOVA statistical test showed a confidence level of 95 %, verifying that the improvements are statistically significant.

Sentiment Data Flow Analysis by Means of Dynamic Linguistic Patterns

Abstract-Emulating the human brain is one of the core challenges of computational intelligence, which entails many key problems of artificial intelligence, including understanding human language, reasoning, and emotions. In this work, computational intelligence techniques are combined with common-sense computing and linguistics to analyze sentiment data flows, i.e., to automatically decode how humans express emotions and opinions via natural language. The increasing availability of social data is extremely beneficial for tasks such as branding, product positioning, corporate reputation management, and social media marketing. The elicitation of useful information from this huge amount of unstructured data, however, remains an open challenge. Although such data are easily accessible to humans, they are not suitable for automatic processing: machines are still unable to effectively and dynamically interpret the meaning associated with natural language text in very large, heterogeneous, noisy, and ambiguous environments such as the Web. We present a novel methodology that goes beyond mere word-level analysis of text and enables a more efficient transformation of unstructured social data into structured information, readily interpretable by machines. In particular, we describe a novel paradigm for real-time concept-level sentiment analysis that blends computational intelligence, linguistics, and common-sense computing in order to improve the accuracy of computationally expensive tasks such as polarity detection from big social data. The main novelty of the paper consists in an algorithm that assigns contextual polarity to concepts in text and flows this polarity through the dependency arcs in order to assign a final polarity label to each sentence. Analyzing how sentiment flows from concept to concept through dependency relations allows for a better understanding of the contextual role of each concept in text, to achieve a dynamic polarity inference that outperforms state-of-the-art statistical methods in terms of both accuracy and training time.

AffectiveSpace 2: Enabling Affective Intuition for Concept-Level Sentiment Analysis

Predicting the affective valence of unknown multi-word expressions is key for concept-level sentiment analysis. AffectiveSpace 2 is a vector space model, built by means of random projection, that allows for reasoning by analogy on natural language con- cepts. By reducing the dimensionality of affec- tive common-sense knowledge, the model allows semantic features associated with concepts to be generalized and, hence, allows concepts to be intu- itively clustered according to their semantic and affective relatedness. Such an affective intuition (so called because it does not rely on explicit fea- tures, but rather on implicit analogies) enables the inference of emotions and polarity conveyed by multi-word expressions, thus achieving efficient concept-level sentiment analysis.

Concept-Level Sentiment Analysis with Dependency-Based Semantic Parsing: A Novel Approach

Sentiment analysis from unstructured natural language text has recently received considerable attention from the research community. In the frame of biologically inspired machine learning approaches, finding good feature sets is particularly challenging yet very important. In this paper, we focus on this fundamental issue of the sentiment analysis task. Specifically, we employ concepts as features and present a concept extraction algorithm based on a novel concept parser scheme to extract semantic features that exploit semantic relationships between words in natural language text. Additional conceptual information of a concept is obtained using the ConceptNet ontology: Concepts extracted from text are sent as queries to ConceptNet to extract their semantics. We select important concepts and eliminate redundant concepts using the Minimum Redundancy and Maximum Relevance feature selection technique. All selected concepts are then used to build a machine learning model that classifies a given document as positive or negative. We evaluate our concept extraction approach using a benchmark movie review dataset provided by Cornell University and product review datasets on books, DVDs, and electronics. Comparative experimental results show that our proposed approach to sentiment analysis outperforms existing state-of-the-art methods.

Propagating and Aggregating Fuzzy Polarities for Concept-Level Sentiment Analysis

An emerging field within sentiment analysis concerns the investigation about how sentiment polarities associated with concepts have to be adapted with respect to the different domains in which they are used. In this paper, we explore the use of fuzzy logic for modeling concept polarities, and the uncertainty associated with them, with respect to different domains. The approach is based on the use of a knowledge graph built by combining two linguistic resources, namely WordNet and SenticNet. Such a knowledge graph is then exploited by a graph-propagation algorithm that propagates sentiment information learned from labeled datasets. The system implementing the proposed approach has been evaluated on the Blitzer dataset. The results demonstrate its viability in real-world cases.

An ELM-based model for affective analogical reasoning

Between the dawn of the Internet through year 2003, there were just a few dozens exabytes of information on the Web. Today, that much information is created weekly. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised increasing interest both in the scientific community, for the exciting open challenges, and in the business world, for the remarkable fallouts in marketing and financial prediction. Keeping up with the ever-growing amount of unstructured information on the Web, however, is a formidable task and requires fast and efficient models for opinion mining. In this paper, we explore how the high generalization performance, low computational complexity, and fast learning speed of extreme learning machines can be exploited to perform analogical reasoning in a vector space model of affective common-sense knowledge. In particular, by enabling a fast reconfiguration of such a vector space, extreme learning machines allow the polarity associated with natural language concepts to be calculated in a more dynamic and accurate way and, hence, perform better concept-level sentiment analysis.

SenticNet 3: A Common and Common-Sense Knowledge Base for Cognition-Driven Sentiment Analysis

SenticNet is a publicly available semantic and affective resource for concept-level sentiment analysis. Rather than using graph-mining and dimensionality-reduction techniques, SenticNet 3 makes use of "energy flows" to connect various parts of extended common and common-sense knowledge representations to one another. SenticNet 3 models nuanced semantics and sentics (that is, the conceptual and affective information associated with multi-word natural language expressions), representing information with a symbolic opacity of an intermediate nature between that of neural networks and typical symbolic systems.

Sentic patterns: Dependency-based rules for concept-level sentiment analysis

The Web is evolving through an era where the opinions of users are getting increasingly important and valuable. The distillation of knowledge from the huge amount of unstructured information on the Web can be a key factor for tasks such as social media marketing, branding, product positioning, and corporate reputation management. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions involves a deep understanding of natural language text by machines, from which we are still very far. To this end, concept-level sentiment analysis aims to go beyond a mere word-level analysis of text and provide novel approaches to opinion mining and sentiment analysis that enable a more efficient passage from (unstructured) textual information to (structured) machine-processable data. A recent knowledge-based technology in this context is sentic computing, which relies on the ensemble application of common-sense computing and the psychology of emotions to infer the conceptual and affective information associated with natural language. Sentic computing, however, is limited by the richness of the knowledge base and by the fact that the bag-of-concepts model, despite more sophisticated than bag-of-words, misses out important discourse structure information that is key for properly detecting the polarity conveyed by natural language opinions. In this work, we introduce a novel paradigm to concept-level sentiment analysis that merges linguistics, common-sense computing, and machine learning for improving the accuracy of tasks such as polarity detection. By allowing sentiments to flow from concept to concept based on the dependency relation of the input sentence, in particular, we achieve a better understanding of the contextual role of each concept within the sentence and, hence, obtain a polarity detection engine that outperforms state-of-the-art statistical methods.

PoliTwi: Early detection of emerging political topics on twitter and the impact on concept-level sentiment analysis

In this work, we present a system called PoliTwi, which was designed to detect emerging political topics (Top Topics) in Twitter sooner than other standard information channels. The recognized Top Topics are shared via different channels with the wider public. For the analysis, we have collected about 4,000,000 tweets before and during the parliamentary election 2013 in Germany, from April until September 2013. It is shown, that new topics appearing in Twitter can be detected right after their occurrence. Moreover, we have compared our results to Google Trends. We observed that the topics emerged earlier in Twitter than in Google Trends.Finally, we show how these topics can be used to extend existing knowledge bases (web ontologies or semantic networks) which are required for concept-level sentiment analysis. For this, we utilized special Twitter hashtags, called sentiment hashtags, used by the German community during the parliamentary election.

Statistical Approaches to Concept-Level Sentiment Analysis

The guest editors introduce novel statistical approaches to concept-level sentiment analysis that go beyond a mere syntactic-driven analysis of text and provide semantic-based methods. Such approaches allow a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain.