Latent Opinions Transfer Network for Target-Oriented Opinion Words Extraction

Target-oriented opinion words extraction (TOWE) is a new subtask of ABSA, which aims to extract the corresponding opinion words for a given opinion target in a sentence. Recently, neural network methods have been applied to this task and achieve promising results. However, the difficulty of annotation causes the datasets of TOWE to be insufficient, which heavily limits the performance of neural models. By contrast, abundant review sentiment classification data are easily available at online review sites. These reviews contain substantial latent opinions information and semantic patterns. In this paper, we propose a novel model to transfer these opinions knowledge from resource-rich review sentiment classification datasets to low-resource task TOWE. To address the challenges in the transfer process, we design an effective transformation method to obtain latent opinions, then integrate them into TOWE. Extensive experimental results show that our model achieves better performance compared to other state-of-the-art methods and significantly outperforms the base model without transferring opinions knowledge. Further analysis validates the effectiveness of our model.Comment: Accepted by the 34th AAAI Conference on Artificial Intelligence (AAAI 2020

Aspect extraction on user textual reviews using multi-channel convolutional neural network

Aspect extraction is a subtask of sentiment analysis that deals with identifying opinion targets in an opinionated text. Existing approaches to aspect extraction typically rely on using handcrafted features, linear and integrated network architectures. Although these methods can achieve good performances, they are time-consuming and often very complicated. In real-life systems, a simple model with competitive results is generally more effective and preferable over complicated models. In this paper, we present a multichannel convolutional neural network for aspect extraction. The model consists of a deep convolutional neural network with two input channels: a word embedding channel which aims to encode semantic information of the words and a part of speech (POS) tag embedding channel to facilitate the sequential tagging process. To get the vector representation of words, we initialized the word embedding channel and the POS channel using pretrained word2vec and one-hot-vector of POS tags, respectively. Both the word embedding and the POS embedding vectors were fed into the convolutional layer and concatenated to a one-dimensional vector, which is finally pooled and processed using a Softmax function for sequence labeling. We finally conducted a series of experiments using four different datasets. The results indicated better performance compared to the baseline models

Aspect and Entity Extraction for Opinion Mining

Abstract: Opinion mining or sentiment analysis is the computational study of people's opinions, appraisals, attitudes, and emotions toward entities such as products, services, organizations, individuals, events, and their different aspects. It has been an active research area in natural language processing and Web mining in recent years. Researchers have studied opinion mining at the document, sentence and aspect levels. Aspect-level (called aspect-based opinion mining) is often desired in practical applications as it provides the detailed opinions or sentiments about different aspects of entities and entities themselves, which are usually required for action. Aspect extraction and entity extraction are thus two core tasks of aspect-based opinion mining. In this chapter, we provide a broad overview of the tasks and the current state-of-the-art extraction techniques

Acquiring Broad Commonsense Knowledge for Sentiment Analysis Using Human Computation

While artificial intelligence is successful in many applications that cover specific domains, for many commonsense problems there is still a large gap with human performance. Automated sentiment analysis is a typical example: while there are techniques that reasonably aggregate sentiments from texts in specific domains, such as online reviews of a particular product category, more general models have a poor performance. We argue that sentiment analysis can be covered more broadly by extending models with commonsense knowledge acquired at scale, using human computation. We study two sentiment analysis problems. We start with document-level sentiment classification, which aims to determine whether a text as a whole expresses a positive or a negative sentiment. We hypothesize that extending classifiers to include the polarities of sentiment words in context can help them scale to broad domains. We also study fine-grained opinion extraction, which aims to pinpoint individual opinions in a text, along with their targets. We hypothesize that extraction models can benefit from broad fine-grained annotations to boost their performance on unfamiliar domains. Selecting sentiment words in context and annotating texts with opinions and targets are tasks that require commonsense knowledge shared by all the speakers of a language. We show how these can be effectively solved through human computation. We illustrate how to define small tasks that can be solved by many independent workers so that results can form a single coherent knowledge base. We also show how to recruit, train, and engage workers, then how to perform effective quality control to obtain sufficiently high-quality knowledge. We show how the resulting knowledge can be effectively integrated into models that scale to broad domains and also perform well in unfamiliar domains. We engage workers through both enjoyment and payment, by designing our tasks as games played for money. We recruit them on a paid crowdsourcing platform where we can reach out to a large pool of active workers. This is an effective recipe for acquiring sentiment knowledge in English, a language that is known by the vast majority of workers on the platform. To acquire sentiment knowledge for other languages, which have received comparatively little attention, we argue that we need to design tasks that appeal to voluntary workers outside the crowdsourcing platform, based on enjoyment alone. However, recruiting and engaging volunteers has been more of an art than a problem that can be solved systematically. We show that combining online advertisement with games, an approach that has been recently proved to work well for acquiring expert knowledge, gives an effective recipe for luring and engaging volunteers to provide good quality sentiment knowledge for texts in French. Our solutions could point the way to how to use human computation to broaden the competence of artificial intelligence systems in other domains as well

Information models in sentiment analysis based on linguistic resources

Почетак новог миленијума обележен је бурним развојем друштвених мрежа, интернет технологијама у облаку и применом вештачке интелигенције у веб алатима. Изузетно брз раст броја текстова на интернету (блогова, сајтова за електронску трговину, форума, дискусионих група, система за пренос кратких порука, друштвених мрежа и портала за објаву вести) увећао је потребу за развојем метода брзе, свеобухватне и прецизне анализе текста. Због тога је значајан развој језичких технологија чији су примарни задаци: класификација докумената (енг. Document classification), груписање докумената (енг. Document clustering), проналажење информација (енг. Information Retrieval), разрешавање значења вишезначних речи (енг. Word-sense disambiguation), екстракција из текста (енг. Text еxtraction), машинско превођење (енг. Machine translation), рачунарско препознавање говора (енг. Computer speech recognition), генерисање природног језика (енг. Natural language generation), анализа осећања (енг. sentiment analysis), итд. У рачунарској лингвистици данас је у употреби више различитих назива за област чији је предмет интересовања обрада осећања у тексту: класификација према осећању (енг. sentiment classification), истраживање мишљење (енг. opinion mining), анализа осећања (енг. sentiment analysis), екстракција осећања (енг. sentiment extraction). По својој природи и методама које користи, анализа осећања у тексту спада у област рачунарске лингвистике која се бави класификацијом текста. У процесу обраде осећања се, у општем случају, говори о три врсте класификације текстова:...The beginning of the new millennium was marked by huge development of social networks, internet technologies in the cloud and applications of artificial intelligence tools on the web. Extremely rapid growth in the number of articles on the Internet (blogs, e-commerce websites, forums, discussion groups, and systems for transmission of short messages, social networks and portals for publishing news) has increased the need for developing methods of rapid, comprehensive and accurate analysis of the text. Therefore, remarkable development of language technologies has enabled their applying in processes of document classification, document clustering, information retrieval, word sense disambiguation, text extraction, machine translation, computer speech recognition, natural language generation, sentiment analysis, etc. In computational linguistics, several different names for the area concerning processing of emotions in text are in use: sentiment classification, opinion mining, sentiment analysis, sentiment extraction. According to the nature and the methods used, sentiment analysis in text belongs to the field of computational linguistics that deals with the classification of text. In the process of analysing of emotions we generally speak of three kinds of text classification:..