Domain-specific Sentiment Lexicon Research Articles

A text analytics model for agricultural knowledge discovery and sustainable food production: A case study from Oklahoma Panhandle

With recent increases in the use of social media in agricultural communities, many farmers are showing more and more interest in participating in social media and sharing different aspects of their profession with peers and policymakers. However, researchers have not explored this valuable data source well enough to help improve agribusiness decision-making. This study aims to investigate the potential capability and richness of social media for agricultural knowledge discovery, which can help monitor, detect, and predict critical agricultural events and activities and develop more sustainable food production and agricultural economy. This research utilizes text-mining tools and techniques to collect, process, and mine unstructured textual data from Twitter. Then, it examines the agreement between the retrieved information from tweets and that from the current monitoring systems and common cultural practices. Our findings illustrate that social media data can effectively provide information regarding the commencement and duration of significant cultural activities This study also examines the classification performance of several popular sentiment analysis tools on the farmer’s tweets and provides suggestions for future research on domain-specific sentiment lexicons for agricultural purposes.

SA-MAIS: Hybrid automatic sentiment analyser for stock market

Sentiment analysis of stock-related tweets is a challenging task, not only due to the specificity of the domain but also because of the short nature of the texts. This work proposes SA-MAIS, a two-step lightweight methodology, specially adapted to perform sentiment analysis in domain-constrained short-text messages. To tackle the issue of domain specificity, based on word frequency, the most relevant words are automatically extracted from the new domain and then manually tagged to update an existing domain-specific sentiment lexicon. The sentiment classification is then performed by combining the updated domain-specific lexicon with VADER sentiment analysis, a well-known and widely used sentiment analysis tool. The proposed method is compared with other well-known and widely used sentiment analysis tools, including transformer-based models, such as BERTweet, Twitter-roBERTa and FinBERT, on a domain-specific corpus of stock market-related tweets comprising 1 million messages. The experimental results show that the proposed approach largely surpasses the performance of the other sentiment analysis tools, reaching an overall accuracy of 72.0%. The achieved results highlight the advantage of using a hybrid method that combines domain-specific lexicons with existing generalist tools for the inference of textual sentiment in domain-specific short-text messages.

An effective multilingual retrieval with query optimization using deep learning technique

• Multilingual retrieval is the information that are retrieved in multiple languages, for the response to the query given by the user. • In a cross lingual search, the user query is given to the various information sources thus enabling the retrieved results of different languages. • The single retrieved results would term to be the results merging problem of multilingual search aspects. • The presented model involves processes namely multilingual search and query optimization. • The stacked autoencoder (SAE) model is used for Multilingual Search process. Multilingual retrieval is the information that are retrieved in multiple languages, for the response to the query given by the user. In a cross lingual search, the user query is given to the various information sources thus enabling the retrieved results of different languages. The single retrieved results would term to be the results merging problem of multilingual search aspects. This paper presents a novel query optimization enabling the user to provide a query in Tamil language and the results are retrieved for across languages. The presented model involves processes namely multilingual search and query optimization. The stacked autoencoder (SAE) model is used for Multilingual Search process. In addition, the query optimization process considered as a NP-hard problem, can be resolved by the use of gray wolf optimization (GWO) algorithm. Besides, global vectors (GloVe) technique is applied to construct the domain-specific sentiment lexicon. An extensive set of experimentation analysis was performed and the results are investigated under distinct aspects. The resultant experimental value of the proposed technique in multilingual search obtains the accuracy of 75% and the optimization algorithm gains the supremacy of the existing algorithms.

Prediction of Box Office for Bollywood Movies Using State-of-the-Art SentiDraw Lexicon for Twitter Analysis

Films are a high-risk industry. Accurate prediction of movie box-office revenues can reduce this market risk and inform the investment decisions regarding promotion of the movie closer to a film’s release or right after release. Studies have shown that chatter on social media platforms like Twitter along with certain movie-related factors can be useful in predicting success of movies. Sentiment of tweets for any movie gives important information about the consumer’s reaction and the polarity of these sentiments has been shown to have an impact on prediction of box-office revenues. This paper presented a novel Bollywood domain specific sentiment lexicon that delivered state-of-the-art performance for polarity determination of reviews. SentiDraw lexicon was built on movie reviews scraped from IMDB and calculated the sentiment orientation of these words by calculating the probability distribution of words across reviews with different star ratings. The results showed that SentiDraw lexicon delivered a superior performance compared to any other lexicon-based method. This significantly contributed in enhancing the prediction accuracy of box office for movies using textual data from Twitter for analysis. In fact, this study demonstrated an extremely parsimonious regression model that used only budget, hype factor, tweet volume, and polarity of tweets for a robust prediction of box office revenues even before the release of a movie.

Lexicon-based sentiment analysis for stock movement prediction

Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We present a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

A Training-Optimization-Based Method for Constructing Domain-Specific Sentiment Lexicon

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.

A Semantic Conceptualization Using Tagged Bag-of-Concepts for Sentiment Analysis

Sentiment could be expressed implicitly or explicitly in the text. Hence, it is the main challenge for current sentiment analysis (SA) approaches to identify hidden sentiments, other common challenges include false classification of opinion words, ignoring context information, and bad handling of a short text that arise from the bad interpretation of the text and lack of enough data required for analysis tasks. In this study, a semantic conceptualization method using tagged bag-of-concepts for SA is proposed to detect the correct sentiment towards the actual target entity that considers all affective and conceptual information conveyed in the text with a special focus on the short text. Tagged bag-of-concepts (TBoC) is a novel approach to analyze and decompose text to uncover latent sentiments while preserving all relations and vital information to boost the accuracy of SA. This study answers questions: Does the information provided via TBoC enhance sentiment classification results on different analysis levels? Is building a structure of concepts increases the accuracy of overall sentiment towards specific opinion target? Does TBoC approach enhance SA results for short text messages? The proposed solution has been applied on two datasets from the restaurant domain, sentiment analysis is performed using the TBoCs structure on multiple levels including document, aspect, aspect-category, and topic levels. TBoC method with domain-specific sentiment lexicon showed exceptional performance and outperformed other state-of-the-art NB, SVM, and NN methods, especially for aspect-level SA. The use of TBoC within the semantic conceptualization model that leverages NLP tasks, Ontology, and semantic methods proved its high capabilities for concept extraction while preserving the information about the context, interrelations, and latent feelings.

Automatic construction of domain-specific sentiment lexicon for unsupervised domain adaptation and sentiment classification

Sentiment analysis has long been suffering from inaccuracies using either machine learning methods that mostly benefit from text features or sentiment lexicon-based methods that are prone to domain-dependent problems. Furthermore, since labeling is a time-consuming and an expensive task, supervised machine learning methods suffer from the drawback of insufficient labeled samples. To tackle the mentioned issues, this paper proposes a novel approach with a hybrid of a neural network and a sentiment lexicon. This combination can simultaneously adapt word polarities to the target domain and leverage the polarity of whole document in order to alleviate the need for large labeled corpora in an unsupervised manner. In this respect, a sentiment lexicon is constructed from the source domain in the preprocessing phase using the labeled data. In the Next phase, having a Multilayer Perceptron (MLP), the weights of the first hidden layer are set to the corresponding polarity of each word from the retrieved sentiment lexicon and the network is trained. Finally, a Domain-Independent Lexicon (DIL) is introduced which contains words (mostly adjectives) with static positive or negative scores independent from a specific domain. After feeding the target domain to the pre-trained model, the total accuracy of the framework is enhanced by estimating the sentiment polarity of each sentence using the summation of the scores of the constitutive domain independent words. The experiments on Amazon multi-domain sentiment dataset illustrate that our approach significantly outperforms several alternative previous approaches of unsupervised domain adaptation.

어휘 관계 및 문맥 정보 기반의 도메인 감성사전 자동 구축 방안 연구

감성사전은 감성 어휘들에 대한 집합으로 각 어휘들에 대한 감성의 극성이 부여되어 있으며, 감성 분석(Sentiment Analysis)을 위한 기초 자료로 활용된다. 하지만 이와 같은 감성 어휘들은 특정 도메인에 따라 극성이 역전되거나 유실될 수도 있으며 의존적인 감성 어휘가 존재할 수 있다. 예를 들면, 일반적으로 ‘잘 잤다’라는 단어는 긍정의 극성을 보이지만, 영화 도메인에서는 그 의미가 부정으로 쓰인다. 그렇기 때문에 감성사전은 분석하고자 하는 도메인의 특징이 반영되어 있어야 하며 도메인에 따라 알맞은 감성사전이 구축되고 활용되어야 한다. 이와 같은 문제를 해결하기 위해 현재 도메인 감성사전을 자동으로 구축하는 다양한 연구들이 나왔지만, 인간의 개입, 문맥적 요소 미고려, 지역적인 정보 반영 등이라는 문제점을 지니고 있다. 본 연구에서는 이와 같은 문제를 해결하기 위해 한국어 범용 감성사전인 ‘KNU 한국어 감성사전’과 글로브 벡터 그리고 접속사 관계를 활용하여, 특정 도메인의 전역적인 감성 정보와 문맥적 특징을 충분히 반영한 도메인 감성사전 구축 방안을 제안한다.

SentiDraw: Using star ratings of reviews to develop domain specific sentiment lexicon for polarity determination

Lexicon based methods use Sentiment Orientation (SO) scores of words contained in the text for polarity determination of documents. These SO scores are obtained from sentiment lexicons like MPQA and SentiWordNet which are built using methods such as Pointwise Mutual Information (PMI) to calculate the SO scores of the words. . The more popular PMI based methods for creating lexicons do not make use of the rich information that can be obtained from star ratings available for text documents such as reviews available for various product categories on online platforms like amazon, yelp.com or IMDB. Star ratings have only recently been used in some studies to calculate SO value of words in reviews for developing domain specific sentiment lexicons. This paper also makes use of star ratings but proposes a novel approach ‘SentiDraw’ where the probability distribution of words across reviews with different star ratings is used to calculate their SO scores. A comprehensive assessment of SentiDraw performance across multiple domains and datasets is also presented by comparing it with other methods that make use of star ratings of reviews for building lexicons. The results show that lexicons built with SentiDraw method delivers superior performance versus other lexicons in six out of nine cases. The accuracy score of sentiment classification using SentiDraw method ranges from 78.0% to 81.6% across domains and SentiDraw lexicon built using Hollywood dataset outperforms any other purely lexicon based method known to authors on the most experimented datasets like Cornell Movie Reviews Dataset (CMRD) and Large Movie Review Dataset (LMRD). Finally, a hybrid approach is also proposed that uses SentiDraw along with supervised methods to deliver state-of-art performance for polarity determination of reviews.

Automatic construction of domain sentiment lexicon for semantic disambiguation

Sentiment lexicon is used to judge the sentiments of words and plays a significant role in sentiment analysis. Existing sentiment lexicons ignore the sentimental ambiguity of words in different contexts and only assign sentiment positive or negative polarity for words. In this paper, we propose an automatic method for the construction of the domain-specific sentiment lexicon (SDS-lex) to avoid sentimental ambiguity, which incorporates the sentiment information not only from the existing lexicons but also from the corpus by using our improved TF-IDF algorithm (ITF-IDF). The ITF-IDF algorithm calculates the sentiment of words by considering both the importance of words and the distribution of different part-of-speech (POS) in a corpus labeled with different sentiment tendencies. Experiments on real-world datasets show that our constructed lexicon improves the sentimental ambiguity and outperforms many existing lexicons in terms of the coverage and the accuracy when performing text sentiment classification tasks.

Sentiment Lexicon Construction With Hierarchical Supervision Topic Model

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).

Cognitive-inspired domain adaptation of sentiment lexicons

Sentiment lexicons are essential tools for polarity classification and opinion mining. In contrast to machine learning methods that only leverage text features or raw text for sentiment analysis, methods that use sentiment lexicons embrace higher interpretability. Although a number of domain-specific sentiment lexicons are made available, it is impractical to build an ex ante lexicon that fully reflects the characteristics of the language usage in endless domains. In this article, we propose a novel approach to simultaneously train a vanilla sentiment classifier and adapt word polarities to the target domain. Specifically, we sequentially track the wrongly predicted sentences and use them as the supervision instead of addressing the gold standard as a whole to emulate the life-long cognitive process of lexicon learning. An exploration-exploitation mechanism is designed to trade off between searching for new sentiment words and updating the polarity score of one word. Experimental results on several popular datasets show that our approach significantly improves the sentiment classification performance for a variety of domains by means of improving the quality of sentiment lexicons. Case-studies also illustrate how polarity scores of the same words are discovered for different domains.

Bootstrap Domain-Specific Sentiment Classifiers from Unlabeled Corpora

There is often the need to perform sentiment classification in a particular domain where no labeled document is available. Although we could make use of a general-purpose off-the-shelf sentiment classifier or a pre-built one for a different domain, the effectiveness would be inferior. In this paper, we explore the possibility of building domain-specific sentiment classifiers with unlabeled documents only. Our investigation indicates that in the word embeddings learned from the unlabeled corpus of a given domain, the distributed word representations (vectors) for opposite sentiments form distinct clusters, though those clusters are not transferable across domains. Exploiting such a clustering structure, we are able to utilize machine learning algorithms to induce a quality domain-specific sentiment lexicon from just a few typical sentiment words (“seeds”). An important finding is that simple linear model based supervised learning algorithms (such as linear SVM) can actually work better than more sophisticated semi-supervised/transductive learning algorithms which represent the state-of-the-art technique for sentiment lexicon induction. The induced lexicon could be applied directly in a lexicon-based method for sentiment classification, but a higher performance could be achieved through a two-phase bootstrapping method which uses the induced lexicon to assign positive/negative sentiment scores to unlabeled documents first, a nd t hen u ses those documents found to have clear sentiment signals as pseudo-labeled examples to train a document sentiment classifier v ia supervised learning algorithms (such as LSTM). On several benchmark datasets for document sentiment classification, our end-to-end pipelined approach which is overall unsupervised (except for a tiny set of seed words) outperforms existing unsupervised approaches and achieves an accuracy comparable to that of fully supervised approaches.

Cross-domain aspect/sentiment-aware abstractive review summarization by combining topic modeling and deep reinforcement learning

Review text has been widely studied in traditional tasks such as sentiment analysis and aspect extraction. However, to date, no work is toward the end-to-end abstractive review summarization that is essential for business organizations and individual consumers to make informed decisions. This study takes the lead to study the aspect/sentiment-aware abstractive review summarization in domain adaptation scenario. Our novel model Abstractive review Summarization with Topic modeling and Reinforcement deep learning (ASTR) leverages the benefits of the supervised deep neural networks, reinforcement learning, and unsupervised probabilistic generative model to strengthen the aspect/sentiment-aware review representation learning. ASTR is a multi-task learning system, which simultaneously optimizes two coupled objectives: domain classification (auxiliary task) and abstractive review summarization (primary task), in which a document modeling module is shared across tasks. The main purpose of our multi-task model is to strengthen the representation learning of documents and safeguard the performance of cross-domain abstractive review summarization. Specifically, ASTR consists of two key components: (1) a domain classifier, working on datasets of both source and target domains to recognize the domain information of texts and transfer knowledge from the source domain to the target domain. In particular, we propose a weakly supervised LDA model to learn the domain-specific aspect and sentiment lexicon representations, which are then fed into the neural hidden states of given reviews to form aspect/sentiment-aware review representations; (2) an abstractive review summarizer, sharing the document modeling module with the domain classifier. The learned aspect/lexicon-aware review representations are fed into a pointer-generator network to generate aspect/sentiment-aware abstractive summaries of given reviews by employing a reinforcement learning algorithm. We conduct extensive experiments on real-life Amazon reviews to evaluate the effectiveness of our model. Quantitatively, ASTR achieves better performance than the state-of-the-art summarization methods in terms of ROUGE score and human evaluation in both out-of-domain and in-domain setups. Qualitatively, our model can generate better sentiment-aware summarization for reviews with different categories and aspects.

Generate domain-specific sentiment lexicon for review sentiment analysis

Lexicon-based approaches for review sentiment analysis have attracted significant attention in recent years. Lots of sentiment lexicon generation methods have been proposed. However, the generation...

Imbalanced text sentiment classification using universal and domain-specific knowledge

In this paper, a sentiment classification model is proposed to address two predominant issues in sentiment classification, namely domain-sensitive and data imbalance. Since words may embed distinct sentiment polarities in different contexts, sentiment classification is widely contended as a domain-sensitive task. Accordingly, this paper draws on label propagation to induce universal and domain-specific sentiment lexicons and builds a domain-adaptive sentiment classification model that incorporates universal and domain-specific knowledge into a unified learning framework. On the flip side, sentiment-related corpuses are usually formed with skewed polarity distribution because individuals tend to share similar assessment criteria on a given object and hence their sentiment polarities toward the same object are likely to be similar. We endeavor to address such imbalanced data problem by advancing a novel over-sampling technique. Unlike existing over-sampling approaches that generate minority-class samples from numerical feature space, the proposed sampling method directly creates synthetic texts from word spaces. Several experiments are conducted to verify the effectiveness of the proposed lexicon generation method, learning framework, and over-sampling method. Results show that the induced sentiment lexicons are interpretable and the proposed model is found to be effective for imbalanced and domain-specific text sentiment classification.

Generate domain-specific sentiment lexicon for review sentiment analysis

Lexicon-based approaches for review sentiment analysis have attracted significant attention in recent years. Lots of sentiment lexicon generation methods have been proposed. However, the generation of domain-specific lexicon with unlabeled data has not been effectively addressed. In this paper, we propose a new domain-specific sentiment lexicon generation method, mutual information is introduced to assign terms with Part-Of-Speech (POS) tags in the lexicon, the training data are selected from unlabeled corpus according to their sentiment scores which are evaluated by the SentiWordNet (SWN) based sentiment classifier. Then we propose a completed lexicon-based sentiment analysis framework which uses the domain-specific sentiment lexicon generated by the proposed domain-specific sentiment lexicon generation method. The experiment is carried out on publically available datasets. Results show that the proposed lexicon-based sentiment analysis framework using domain-specific lexicons generated by the proposed method gets a good performance.

Automatic Approach of Sentiment Lexicon Generation for Mobile Shopping Reviews

The dramatic increase in the use of smartphones has allowed people to comment on various products at any time. The analysis of the sentiment of users’ product reviews largely depends on the quality of sentiment lexicons. Thus, the generation of high‐quality sentiment lexicons is a critical topic. In this paper, we propose an automatic approach for constructing a domain‐specific sentiment lexicon by considering the relationship between sentiment words and product features in mobile shopping reviews. The approach first selects sentiment words and product features from original reviews and mines the relationship between them using an improved pointwise mutual information algorithm. Second, sentiment words that are related to mobile shopping are clustered into categories to form sentiment dimensions. At each sentiment dimension, each sentiment word can take the value of 0 or 1, where 1 indicates that the word belongs to a particular category whereas 0 indicates that it does not belong to that category. The generated lexicon is evaluated by constructing a sentiment classification task using several product reviews written in both Chinese and English. Two popular non‐domain‐specific sentiment lexicons as well as state‐of‐the‐art machine‐learning and deep‐learning models are chosen as benchmarks, and the experimental results show that our sentiment lexicons outperform the benchmarks with statistically significant differences, thus proving the effectiveness of the proposed approach.

Performance Evaluation of Domain-Specific Sentiment Dictionary Construction Methods for Opinion Mining

Sentiment dictionaries or lexicons are core elements for “bag-of-word” approaches of opinion mining or sentiment analysis. Rather than using general-purpose sentiment dictionaries, domain-specific sentiment lexicons can contribute to improve performance because they can reflect domain specific terms and meanings. This paper presents four domain-specific sentiment dictionary construction methods for opinion mining, and describes performance evaluation results using a practical data set. The comparison subjects of this research include SO-PMI (Semantic Orientation from Pointwise Mutual Information) and three term frequency-based methods with different term polarity measures. To evaluate the performance of four different methods, a movie review data set from a representative Internet movie community site, IMDb (Internet Movie Database) is collected using a web crawling program, and is analyzed using R programs. Based on training data set, domain specific sentiment dictionaries are constructed using four different methods, and are compared their performance of sentiment analysis. The experimental results show that domain-specific sentiment dictionaries are working better than general-purpose dictionaries except one genre, „animation‟. Also, term frequency-based approaches show better performance than SO-PMI.