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1.
《Information processing & management》2016,52(1):5-19
Sentiment analysis on Twitter has attracted much attention recently due to its wide applications in both, commercial and public sectors. In this paper we present SentiCircles, a lexicon-based approach for sentiment analysis on Twitter. Different from typical lexicon-based approaches, which offer a fixed and static prior sentiment polarities of words regardless of their context, SentiCircles takes into account the co-occurrence patterns of words in different contexts in tweets to capture their semantics and update their pre-assigned strength and polarity in sentiment lexicons accordingly. Our approach allows for the detection of sentiment at both entity-level and tweet-level. We evaluate our proposed approach on three Twitter datasets using three different sentiment lexicons to derive word prior sentiments. Results show that our approach significantly outperforms the baselines in accuracy and F-measure for entity-level subjectivity (neutral vs. polar) and polarity (positive vs. negative) detections. For tweet-level sentiment detection, our approach performs better than the state-of-the-art SentiStrength by 4–5% in accuracy in two datasets, but falls marginally behind by 1% in F-measure in the third dataset. 相似文献
2.
《Information processing & management》2019,56(4):1245-1259
Sentiment analysis concerns the study of opinions expressed in a text. Due to the huge amount of reviews, sentiment analysis plays a basic role to extract significant information and overall sentiment orientation of reviews. In this paper, we present a deep-learning-based method to classify a user's opinion expressed in reviews (called RNSA).To the best of our knowledge, a deep learning-based method in which a unified feature set which is representative of word embedding, sentiment knowledge, sentiment shifter rules, statistical and linguistic knowledge, has not been thoroughly studied for a sentiment analysis. The RNSA employs the Recurrent Neural Network (RNN) which is composed by Long Short-Term Memory (LSTM) to take advantage of sequential processing and overcome several flaws in traditional methods, where order and information about the word are vanished. Furthermore, it uses sentiment knowledge, sentiment shifter rules and multiple strategies to overcome the following drawbacks: words with similar semantic context but opposite sentiment polarity; contextual polarity; sentence types; word coverage limit of an individual lexicon; word sense variations. To verify the effectiveness of our work, we conduct sentence-level sentiment classification on large-scale review datasets. We obtained encouraging result. Experimental results show that (1) feature vectors in terms of (a) statistical, linguistic and sentiment knowledge, (b) sentiment shifter rules and (c) word-embedding can improve the classification accuracy of sentence-level sentiment analysis; (2) our method that learns from this unified feature set can obtain significant performance than one that learns from a feature subset; (3) our neural model yields superior performance improvements in comparison with other well-known approaches in the literature. 相似文献
3.
《Information processing & management》2020,57(6):102290
In this paper we introduce HEMOS (Humor-EMOji-Slang-based) system for fine-grained sentiment classification for the Chinese language using deep learning approach. We investigate the importance of recognizing the influence of humor, pictograms and slang on the task of affective processing of the social media. In the first step, we collected 576 frequent Internet slang expressions as a slang lexicon; then, we converted 109 Weibo emojis into textual features creating a Chinese emoji lexicon. In the next step, by performing two polarity annotations with new “optimistic humorous type” and “pessimistic humorous type” added to standard “positive” and “negative” sentiment categories, we applied both lexicons to attention-based bi-directional long short-term memory recurrent neural network (AttBiLSTM) and tested its performance on undersized labeled data. Our experimental results show that the proposed method can significantly improve the state-of-the-art methods in predicting sentiment polarity on Weibo, the largest Chinese social network. 相似文献
4.
《Information processing & management》2020,57(6):102273
Vital to the task of Sentiment Analysis (SA), or automatically mining sentiment expression from text, is a sentiment lexicon. This fundamental lexical resource comprises the smallest sentiment-carrying units of text, words, annotated for their sentiment properties, and aids in SA tasks on larger pieces of text. Unfortunately, digital dictionaries do not readily include information on the sentiment properties of their entries, and manually compiling sentiment lexicons is tedious in terms of annotator time and effort. This has resulted in the emergence of a large number of research works concentrated on automated sentiment lexicon generation. The dictionary-based approach involves leveraging digital dictionaries, while the corpus-based approach involves exploiting co-occurrence statistics embedded in text corpora. Although the former approach has been exhaustively investigated, the majority of works focus on terms. The few state-of-the-art models concentrated on the finer-grained term sense level remain to exhibit several prominent limitations, e.g., the proposed semantic relations algorithm retrieves only senses that are at a close proximity to the seed senses in the semantic network, thus prohibiting the retrieval of remote sentiment-carrying senses beyond the reach of the ‘radius’ defined by number of iterations of semantic relations expansion. The proposed model aims to overcome the issues inherent in dictionary-based sense-level sentiment lexicon generation models using: (1) null seed sets, and a morphological approach inspired by the Marking Theory in Linguistics to populate them automatically; (2) a dual-step context-aware gloss expansion algorithm that ‘mines’ human defined gloss information from a digital dictionary, ensuring senses overlooked by the semantic relations expansion algorithm are identified; and (3) a fully-unsupervised sentiment categorization algorithm on the basis of the Network Theory. The results demonstrate that context-aware in-gloss matching successfully retrieves senses beyond the reach of the semantic relations expansion algorithm used by prominent, well-known models. Evaluation of the proposed model to accurately assign senses with polarity demonstrates that it is on par with state-of-the-art models against the same gold standard benchmarks. The model has theoretical implications in future work to effectively exploit the readily-available human-defined gloss information in a digital dictionary, in the task of assigning polarity to term senses. Extrinsic evaluation in a real-world sentiment classification task on multiple publically-available varying-domain datasets demonstrates its practical implication and application in sentiment analysis, as well as in other related fields such as information science, opinion retrieval and computational linguistics. 相似文献
5.
Asad Abdi Siti Mariyam Shamsuddin Ramiz M. Aliguliyev 《Information processing & management》2018,54(2):318-338
Sentiment analysis concerns the study of opinions expressed in a text. This paper presents the QMOS method, which employs a combination of sentiment analysis and summarization approaches. It is a lexicon-based method to query-based multi-documents summarization of opinion expressed in reviews.QMOS combines multiple sentiment dictionaries to improve word coverage limit of the individual lexicon. A major problem for a dictionary-based approach is the semantic gap between the prior polarity of a word presented by a lexicon and the word polarity in a specific context. This is due to the fact that, the polarity of a word depends on the context in which it is being used. Furthermore, the type of a sentence can also affect the performance of a sentiment analysis approach. Therefore, to tackle the aforementioned challenges, QMOS integrates multiple strategies to adjust word prior sentiment orientation while also considers the type of sentence. QMOS also employs the Semantic Sentiment Approach to determine the sentiment score of a word if it is not included in a sentiment lexicon.On the other hand, the most of the existing methods fail to distinguish the meaning of a review sentence and user's query when both of them share the similar bag-of-words; hence there is often a conflict between the extracted opinionated sentences and users’ needs. However, the summarization phase of QMOS is able to avoid extracting a review sentence whose similarity with the user's query is high but whose meaning is different. The method also employs the greedy algorithm and query expansion approach to reduce redundancy and bridge the lexical gaps for similar contexts that are expressed using different wording, respectively. Our experiment shows that the QMOS method can significantly improve the performance and make QMOS comparable to other existing methods. 相似文献
6.
Although deep learning breakthroughs in NLP are based on learning distributed word representations by neural language models, these methods suffer from a classic drawback of unsupervised learning techniques. Furthermore, the performance of general-word embedding has been shown to be heavily task-dependent. To tackle this issue, recent researches have been proposed to learn the sentiment-enhanced word vectors for sentiment analysis. However, the common limitation of these approaches is that they require external sentiment lexicon sources and the construction and maintenance of these resources involve a set of complexing, time-consuming, and error-prone tasks. In this regard, this paper proposes a method of sentiment lexicon embedding that better represents sentiment word's semantic relationships than existing word embedding techniques without manually-annotated sentiment corpus. The major distinguishing factor of the proposed framework was that joint encoding morphemes and their POS tags, and training only important lexical morphemes in the embedding space. To verify the effectiveness of the proposed method, we conducted experiments comparing with two baseline models. As a result, the revised embedding approach mitigated the problem of conventional context-based word embedding method and, in turn, improved the performance of sentiment classification. 相似文献
7.
Sentiment analysis concerns about automatically identifying sentiment or opinion expressed in a given piece of text. Most prior work either use prior lexical knowledge defined as sentiment polarity of words or view the task as a text classification problem and rely on labeled corpora to train a sentiment classifier. While lexicon-based approaches do not adapt well to different domains, corpus-based approaches require expensive manual annotation effort. 相似文献
8.
《Information processing & management》2023,60(2):103192
The replies of people seeking support in online mental health communities can be analyzed to discover if they feel better after receiving support; feeling better indicates a cognitive change. Most research uses key phrase matching and word frequency statistics to identify psychological cognitive change, methods that result in omissions and inaccuracy. This study constructs an intelligent method for identifying psychological cognitive change based on natural language processing technology. It incorporates information related to emotions that appears in reply text to help identify whether psychological cognitive change has occurred. The model first encodes the emotion information based on rule matching and manual annotation, then adds the encoded emotion lexicon and a cognitive change lexicon to a word2vec high-dimensional semantic word vector training, converts the annotated cognitive change recognition text into a vector matrix using the trained model, and train in the annotated text using TextCNN. To compare the results with those of the traditional methods (key phrase matching and sentiment word frequency statistics), this study uses a semi-automated approach to construct a lexicon of psychological cognitive change, as well as a keyword lexicon without cognitive change, based on word vectors and similarity. We compare the performance of the classifier before and after the fusion of the graphical emotion information, compare the LSTM and Transformer as baselines, and compare traditional word frequency statistics methods. The experimental results show that our proposed classification model performs better than the others; it achieves 84.38% precision, an 84.09% recall rate, and an 84.17% F1 value. Our work bears methodological implications for online mental health platforms. 相似文献
9.
《Information processing & management》2019,56(6):102079
The emergence of social media and the huge amount of opinions that are posted everyday have influenced online reputation management. Reputation experts need to filter and control what is posted online and, more importantly, determine if an online post is going to have positive or negative implications towards the entity of interest. This task is challenging, considering that there are posts that have implications on an entity's reputation but do not express any sentiment. In this paper, we propose two approaches for propagating sentiment signals to estimate reputation polarity of tweets. The first approach is based on sentiment lexicons augmentation, whereas the second is based on direct propagation of sentiment signals to tweets that discuss the same topic. In addition, we present a polar fact filter that is able to differentiate between reputation-bearing and reputation-neutral tweets. Our experiments indicate that weakly supervised annotation of reputation polarity is feasible and that sentiment signals can be propagated to effectively estimate the reputation polarity of tweets. Finally, we show that learning PMI values from the training data is the most effective approach for reputation polarity analysis. 相似文献
10.
Automatic text classification is the task of organizing documents into pre-determined classes, generally using machine learning algorithms. Generally speaking, it is one of the most important methods to organize and make use of the gigantic amounts of information that exist in unstructured textual format. Text classification is a widely studied research area of language processing and text mining. In traditional text classification, a document is represented as a bag of words where the words in other words terms are cut from their finer context i.e. their location in a sentence or in a document. Only the broader context of document is used with some type of term frequency information in the vector space. Consequently, semantics of words that can be inferred from the finer context of its location in a sentence and its relations with neighboring words are usually ignored. However, meaning of words, semantic connections between words, documents and even classes are obviously important since methods that capture semantics generally reach better classification performances. Several surveys have been published to analyze diverse approaches for the traditional text classification methods. Most of these surveys cover application of different semantic term relatedness methods in text classification up to a certain degree. However, they do not specifically target semantic text classification algorithms and their advantages over the traditional text classification. In order to fill this gap, we undertake a comprehensive discussion of semantic text classification vs. traditional text classification. This survey explores the past and recent advancements in semantic text classification and attempts to organize existing approaches under five fundamental categories; domain knowledge-based approaches, corpus-based approaches, deep learning based approaches, word/character sequence enhanced approaches and linguistic enriched approaches. Furthermore, this survey highlights the advantages of semantic text classification algorithms over the traditional text classification algorithms. 相似文献
11.
《Information processing & management》2022,59(3):102883
Sarcasm expression is a pervasive literary technique in which people intentionally express the opposite of what is implied. Accurate detection of sarcasm in a text can facilitate the understanding of speakers’ true intentions and promote other natural language processing tasks, especially sentiment analysis tasks. Since sarcasm is a kind of implicit sentiment expression and speakers deliberately confuse the audience, it is challenging to detect sarcasm only by text. Existing approaches based on machine learning and deep learning achieved unsatisfactory performance when handling sarcasm text with complex expression or needing specific background knowledge to understand. Especially, due to the characteristics of the Chinese language itself, sarcasm detection in Chinese is more difficult. To alleviate this dilemma on Chinese sarcasm detection, we propose a sememe and auxiliary enhanced attention neural model, SAAG. At the word level, we introduce sememe knowledge to enhance the representation learning of Chinese words. Sememe is the minimum unit of meaning, which is a fine-grained portrayal of a word. At the sentence level, we leverage some auxiliary information, such as the news title, to learning the representation of the context and background of sarcasm expression. Then, we construct the representation of text expression progressively and dynamically. The evaluation on a sarcasm dateset, consisting of comments on news text, reveals that our proposed approach is effective and outperforms the state-of-the-art models. 相似文献
12.
13.
《Information processing & management》2016,52(1):36-45
The polarity shift problem is a major factor that affects classification performance of machine-learning-based sentiment analysis systems. In this paper, we propose a three-stage cascade model to address the polarity shift problem in the context of document-level sentiment classification. We first split each document into a set of subsentences and build a hybrid model that employs rules and statistical methods to detect explicit and implicit polarity shifts, respectively. Secondly, we propose a polarity shift elimination method, to remove polarity shift in negations. Finally, we train base classifiers on training subsets divided by different types of polarity shifts, and use a weighted combination of the component classifiers for sentiment classification. The results on a range of experiments illustrate that our approach significantly outperforms several alternative methods for polarity shift detection and elimination. 相似文献
14.
《Information processing & management》2020,57(5):102221
Although statistical learning methods have achieved success in e-commerce platform product review sentiment classification, two problems have limited its practical application: 1) The computational efficiency to process large-scale reviews; 2) the ability to continuously learn from increasing reviews and multiple domains. This paper presents a continuous naïve Bayes learning framework for large-scale and multi-domain e-commerce platform product review sentiment classification. While keeping the high computational efficiency of the traditional naïve Bayes model, we extend the parameter estimation mechanism in naïve Bayes to a continuous learning style. We furthermore propose ways to fine-tune the learned distribution based on three kinds of assumptions to better adapt to different domains. Experimental results on the Amazon product and movie review sentiment datasets show that our model can use the knowledge learned from past domains to guide learning in new domains, and has a better capacity of dealing with reviews that are continuously updated and come from different domains. 相似文献
15.
《Information processing & management》2023,60(2):103230
As a hot spot these years, cross-domain sentiment classification aims to learn a reliable classifier using labeled data from a source domain and evaluate the classifier on a target domain. In this vein, most approaches utilized domain adaptation that maps data from different domains into a common feature space. To further improve the model performance, several methods targeted to mine domain-specific information were proposed. However, most of them only utilized a limited part of domain-specific information. In this study, we first develop a method of extracting domain-specific words based on the topic information derived from topic models. Then, we propose a Topic Driven Adaptive Network (TDAN) for cross-domain sentiment classification. The network consists of two sub-networks: a semantics attention network and a domain-specific word attention network, the structures of which are based on transformers. These sub-networks take different forms of input and their outputs are fused as the feature vector. Experiments validate the effectiveness of our TDAN on sentiment classification across domains. Case studies also indicate that topic models have the potential to add value to cross-domain sentiment classification by discovering interpretable and low-dimensional subspaces. 相似文献
16.
《Information processing & management》2020,57(6):102346
Polarity classification is one of the most fundamental problems in sentiment analysis. In this paper, we propose a novel method, Sound Cosine Similaritye Matching, for polarity classification of Twitter messages which incorporates features based on audio data rather than on grammar or other text properties, i.e., eliminates the dependency on external dictionaries. It is useful especially for correctly identifying misspelled or shortened words that are frequently encountered in text from online social media. Method performance is evaluated in two levels: i) capture rate of the misspelled and shortened words, ii) classification performance of the feature set. Our results show that classification accuracy is improved, compared to two other models in the literature, when the proposed features are used. 相似文献
17.
《Information processing & management》2023,60(5):103424
It is widely assumed that gendered wording in job advertisements can be a source of unconscious gender bias that contributes to occupational gender segregation, and gender lexicons have been developed and employed to detect gendered wording in job advertisements. The goal of this study is to create a Chinese job advertisement gender lexicon, the lack of which has impeded related research in China. Based on 53,786 job advertisements collected from a large employment website in China, the lexicon creation process enabled by supervised learning mainly involved identifying candidate gender words and determining their gender scores. The combination of Word2Vec and SVR yielded the highest performance and generated a new Chinese gender lexicon consisting of 1,429 masculine words and 1,064 feminine words with varying gender scores. The lexicon was successfully applied in gendered wording detection, revealing that masculinely and femininely worded job advertisements dominated different occupations in China. The superiority of the proposed lexicon creation method and the resultant lexicon are verified through comparisons. 相似文献
18.
Opinion mining in a multilingual and multi-domain environment as YouTube requires models to be robust across domains as well as languages, and not to rely on linguistic resources (e.g. syntactic parsers, POS-taggers, pre-defined dictionaries) which are not always available in many languages. In this work, we i) proposed a convolutional N-gram BiLSTM (CoNBiLSTM) word embedding which represents a word with semantic and contextual information in short and long distance periods; ii) applied CoNBiLSTM word embedding for predicting the type of a comment, its polarity sentiment (positive, neutral or negative) and whether the sentiment is directed toward the product or video; iii) evaluated the efficiency of our model on the SenTube dataset, which contains comments from two domains (i.e. automobile, tablet) and two languages (i.e. English, Italian). According to the experimental results, CoNBiLSTM generally outperforms the approach using SVM with shallow syntactic structures (STRUCT) – the current state-of-the-art sentiment analysis on the SenTube dataset. In addition, our model achieves more robustness across domains than the STRUCT (e.g. 7.47% of the difference in performance between the two domains for our model vs. 18.8% for the STRUCT) 相似文献
19.
Due to natural language morphology, words can take on various morphological forms. Morphological normalisation – often used in information retrieval and text mining systems – conflates morphological variants of a word to a single representative form. In this paper, we describe an approach to lexicon-based inflectional normalisation. This approach is in between stemming and lemmatisation, and is suitable for morphological normalisation of inflectionally complex languages. To eliminate the immense effort required to compile the lexicon by hand, we focus on the problem of acquiring automatically an inflectional morphological lexicon from raw corpora. We propose a convenient and highly expressive morphology representation formalism on which the acquisition procedure is based. Our approach is applied to the morphologically complex Croatian language, but it should be equally applicable to other languages of similar morphological complexity. Experimental results show that our approach can be used to acquire a lexicon whose linguistic quality allows for rather good normalisation performance. 相似文献