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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
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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
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《Information processing & management》2020,57(3):102211
Aspect-based sentiment analysis allows one to compute the sentiment for an aspect in a certain context. One problem in this analysis is that words possibly carry different sentiments for different aspects. Moreover, an aspect’s sentiment might be highly influenced by the domain-specific knowledge. In order to tackle these issues, in this paper, we propose a hybrid solution for sentence-level aspect-based sentiment analysis using A Lexicalized Domain Ontology and a Regularized Neural Attention model (ALDONAr). The bidirectional context attention mechanism is introduced to measure the influence of each word in a given sentence on an aspect’s sentiment value. The classification module is designed to handle the complex structure of a sentence. The manually created lexicalized domain ontology is integrated to utilize the field-specific knowledge. Compared to the existing ALDONA model, ALDONAr uses BERT word embeddings, regularization, the Adam optimizer, and different model initialization. Moreover, its classification module is enhanced with two 1D CNN layers providing superior results on standard datasets. 相似文献
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Frank Z. Xing Filippo Pallucchini Erik Cambria 《Information processing & management》2019,56(3):554-564
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. 相似文献
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Electronic word of mouth (eWOM) is prominent and abundant in consumer domains. Both consumers and product/service providers need help in understanding and navigating the resulting information spaces, which are vast and dynamic. The general tone or polarity of reviews, blogs or tweets provides such help. In this paper, we explore the viability of automatic sentiment analysis (SA) for assessing the polarity of a product or a service review. To do so, we examine the potential of the major approaches to sentiment analysis, along with star ratings, in capturing the true sentiment of a review. We further model contextual factors (specifically, product type and review length) as two moderators affecting SA accuracy. The results of our analysis of 900 reviews suggest that different tools representing the main approaches to SA display differing levels of accuracy, yet overall, SA is very effective in detecting the underlying tone of the analyzed content, and can be used as a complement or an alternative to star ratings. The results further reveal that contextual factors such as product type and review length, play a role in affecting the ability of a technique to reflect the true sentiment of a review. 相似文献
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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
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《Information processing & management》2022,59(5):103048
Existing methods for text generation usually fed the overall sentiment polarity of a product as an input into the seq2seq model to generate a relatively fluent review. However, these methods cannot express more fine-grained sentiment polarity. Although some studies attempt to generate aspect-level sentiment controllable reviews, the personalized attribute of reviews would be ignored. In this paper, a hierarchical template-transformer model is proposed for personalized fine-grained sentiment controllable generation, which aims to generate aspect-level sentiment controllable reviews with personalized information. The hierarchical structure can effectively learn sentiment information and lexical information separately. The template transformer uses a part of speech (POS) template to guide the generation process and generate a smoother review. To verify our model, we used the existing model to obtain a corpus named FSCG-80 from Yelp, which contains 800K samples and conducted a series of experiments on this corpus. Experimental results show that our model can achieve up to 89.93% aspect-sentiment control accuracy and generate more fluent reviews. 相似文献
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《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
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[目的/意义] 随着"互联网+"在医疗服务行业的应用与发展,积累了大量的医疗评价信息,利用情感分析技术可以对其进行有效地挖掘和利用,从而为医疗管理提供决策参考。[方法/过程] 基于框架语义理论建立医疗情感语义分类词典;采用词典和规则相结合的方法进行在线医疗评论的情感语义分析,标注情感类别、情感主题、极性和强度等信息。[结果/结论] 通过在线医疗评论数据测试,验证了研究方法的有效性和科学性,是情感分析向医疗健康领域纵深发展的一次有益探索。 相似文献
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Ideation is an important phase in the new product development process at which product designers innovate and select novel ideas that can be added as features to an existing product. One way to find novel ideas is to transfer uncommon features of products of other domains and integrate them into the product to be improved. However, before incorporating such targeted features into the product, they need to be evaluated against the customers’ acceptance in social media using sentiment aggregation tools. Despite the many studies in sentiment analysis, mapping the customers’ opinions towards both high-level and technical features of a product extracted from social media to their best corresponding component in that product is still a challenge. Furthermore, none of the existing approaches ascertains the sentiment value of a targeted feature by capturing its dependencies on other features. In this paper, to address these drawbacks, we propose the sentiment aggregation framework for targeted features (SA-TF). SA-TF determines the sentiment of a targeted feature by assisting product designers in the tasks of mapping the features discussed in the reviews to the right product components, sentiment aggregation and considering feature dependencies to determine their polarity. The superiority of the different phases of SA-TF is demonstrated with experiments and comparing it with an existing approach. 相似文献
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《Information processing & management》2023,60(2):103187
The increasing interest around emotions in online texts creates the demand for financial sentiment analysis. Previous studies mainly focus on coarse-grained document-/sentence-level sentiment analysis, which ignores different sentiment polarities of various targets (e.g., company entities) in a sentence. To fill the gap, from a fine-grained target-level perspective, we propose a novel Lexicon Enhanced Collaborative Network (LECN) for targeted sentiment analysis (TSA) in financial texts. In general, the model designs a unified and collaborative framework that can capture the associations of targets and sentiment cues to enhance the overall performance of TSA. Moreover, the model dynamically incorporates sentiment lexicons to guide the sentiment classification, which cultivates the model faculty of understanding financial expressions. In addition, the model introduces a message selective-passing mechanism to adaptively control the information flow between two tasks, thereby improving the collaborative effects. To verify the effectiveness of LECN, we conduct experiments on four financial datasets, including SemEVAL2017 Task5 subset1, SemEVAL2017 Task5 subset2, FiQA 2018 Task1, and Financial PhraseBank. Results show that LECN achieves improvements over the state-of-art baseline by 1.66 p.p., 1.47 p.p., 1.94 p.p., and 1.88 p.p. in terms of F1-score. A series of further analyses also indicate that LECN has a better capacity for comprehending domain-specific expressions and can achieve the mutually beneficial effect between tasks. 相似文献
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Aspect-based sentiment analysis aims to predict the sentiment polarities of specific targets in a given text. Recent researches show great interest in modeling the target and context with attention network to obtain more effective feature representation for sentiment classification task. However, the use of an average vector of target for computing the attention score for context is unfair. Besides, the interaction mechanism is simple thus need to be further improved. To solve the above problems, this paper first proposes a coattention mechanism which models both target-level and context-level attention alternatively so as to focus on those key words of targets to learn more effective context representation. On this basis, we implement a Coattention-LSTM network which learns nonlinear representations of context and target simultaneously and can extracts more effective sentiment feature from coattention mechanism. Further, a Coattention-MemNet network which adopts a multiple-hops coattention mechanism is proposed to improve the sentiment classification result. Finally, we propose a new location weighted function which considers the location information to enhance the performance of coattention mechanism. Extensive experiments on two public datasets demonstrate the effectiveness of all proposed methods, and our findings in the experiments provide new insight for future developments of using attention mechanism and deep neural network for aspect-based sentiment analysis. 相似文献
13.
There is a strong interest among academics and practitioners in studying branding issues in the big data era. In this article, we examine the sentiments toward a brand, via brand authenticity, to identify the reasons for positive or negative sentiments on social media. Moreover, in order to increase precision, we investigate sentiment polarity on a five-point scale. From a database containing 2,282,912 English tweets with the keyword ‘Starbucks’, we use a set of 2204 coded tweets both for analyzing brand authenticity and sentiment polarity. First, we examine the tweets qualitatively to gain insights about brand authenticity sentiments. Then we analyze the data quantitatively to establish a framework in which we predict both the brand authenticity dimensions and their sentiment polarity. Through three qualitative studies, we discuss several tweets from the dataset that can be classified under the quality commitment, heritage, uniqueness, and symbolism categories. Using latent semantic analysis (LSA), we extract the common words in each category. We verify the robustness of previous findings with an in-lab experiment. Results from the support vector machine (SVM), as the quantitative research method, illustrate the effectiveness of the proposed procedure of brand authenticity sentiment analysis. It shows high accuracy for both the brand authenticity dimensions’ predictions and their sentiment polarity. We then discuss the theoretical and managerial implications of the studies. 相似文献
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[目的/意义] 提出一种基于在线产品评论的竞争情报挖掘框架,为企业改进产品设计和制定竞争策略提供参考。[方法/过程] 利用Word2vec技术构建产品特征词集合,识别用户评论主题特征。然后使用情感分析方法对评论文本进行分类,得到特征维度的评论情感。最后从产品主题特征和情感态度特征两方面进行数据分析,并以可视化结果呈现。[结果/结论] 以汽车行业的评论数据为例进行实验,结果表明该方法能够有效提取产品情报信息,帮助企业有效识别自身品牌及竞争对手的优势和劣势,为大数据环境下的竞争情报挖掘提供方法指导。 相似文献
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《Information processing & management》2019,56(2):291-307
In spite of the vast amount of work on subjectivity and sentiment analysis (SSA), it is not yet particularly clear how lexical information can best be modeled in a morphologically-richness language. To bridge this gap, we report successful models targeting lexical input in Arabic, a language of very complex morphology. Namely, we measure the impact of both gold and automatic segmentation on the task and build effective models achieving significantly higher than our baselines. Our models exploiting predicted segments improve subjectivity classification by 6.02% F1-measure and sentiment classification by 4.50% F1-measure against the majority class baseline surface word forms. We also perform in-depth (error) analyses of the behavior of the models and provide detailed explanations of subjectivity and sentiment expression in Arabic against the morphological richness background in which the work is situated. 相似文献
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Shiwei Zhang Xiuzhen Zhang Jeffrey Chan Paolo Rosso 《Information processing & management》2019,56(5):1633-1644
Irony as a literary technique is widely used in online texts such as Twitter posts. Accurate irony detection is crucial for tasks such as effective sentiment analysis. A text’s ironic intent is defined by its context incongruity. For example in the phrase “I love being ignored”, the irony is defined by the incongruity between the positive word “love” and the negative context of “being ignored”. Existing studies mostly formulate irony detection as a standard supervised learning text categorization task, relying on explicit expressions for detecting context incongruity. In this paper we formulate irony detection instead as a transfer learning task where supervised learning on irony labeled text is enriched with knowledge transferred from external sentiment analysis resources. Importantly, we focus on identifying the hidden, implicit incongruity without relying on explicit incongruity expressions, as in “I like to think of myself as a broken down Justin Bieber – my philosophy professor.” We propose three transfer learning-based approaches to using sentiment knowledge to improve the attention mechanism of recurrent neural models for capturing hidden patterns for incongruity. Our main findings are: (1) Using sentiment knowledge from external resources is a very effective approach to improving irony detection; (2) For detecting implicit incongruity, transferring deep sentiment features seems to be the most effective way. Experiments show that our proposed models outperform state-of-the-art neural models for irony detection. 相似文献
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《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