Constructing domain-dependent sentiment dictionary for sentiment analysis
TL;DR: A weak supervised neural model that aims at learning a set of sentiment clusters embedding from the sentence global representation of the target domain, and an attention-based LSTM model to address aspect-level sentiment analysis task based on the sentiment score retrieved from the proposed dictionary.
Abstract: Sentiment dictionary is of great value to sentiment analysis, which is used widely in sentiment analysis compositionality. However, the sentiment polarity and intensity of the word may vary from one domain to another. In this paper, we introduce a novel approach to build domain-dependent sentiment dictionary, SentiDomain. We propose a weak supervised neural model that aims at learning a set of sentiment clusters embedding from the sentence global representation of the target domain. The model is trained on unlabeled data with weak supervision by reconstructing the input sentence representation from the resulting representation. Furthermore, we also propose an attention-based LSTM model to address aspect-level sentiment analysis task based on the sentiment score retrieved from the proposed dictionary. The key idea is to weight-down the non-sentiment parts among aspect-related information in a given sentence. Our extensive experiments on both English and Chinese benchmark datasets have shown that compared to the state-of-the-art alternatives, our proposals can effectively improve polarity detection.
Citations
More filters
01 Jan 2013
TL;DR: This book gives a comprehensive view of state-of-the-art techniques that are used to build spoken dialogue systems and presents dialogue modelling and system development issues relevant in both academic and industrial environments and also discusses requirements and challenges for advanced interaction management and future research.
Abstract: Considerable progress has been made in recent years in the development of dialogue systems that support robust and efficient human–machine interaction using spoken language. Spoken dialogue technology allows various interactive applications to be built and used for practical purposes, and research focuses on issues that aim to increase the system’s communicative competence by including aspects of error correction, cooperation, multimodality, and adaptation in context. This book gives a comprehensive view of state-of-the-art techniques that are used to build spoken dialogue systems. It provides an overview of the basic issues such as system architectures, various dialogue management methods, system evaluation, and also surveys advanced topics concerning extensions of the basic model to more conversational setups. The goal of the book is to provide an introduction to the methods, problems, and solutions that are used in dialogue system development and evaluation. It presents dialogue modelling and system development issues relevant in both academic and industrial environments and also discusses requirements and challenges for advanced interaction management and future research. vi KEywoRDS Spoken dialogue systems, multimodality, evaluation, error-handling, dialogue management, statistical method v MC_Jok nen_FM. ndd Achorn Internat onal 10/10/2009 04:18AM
304 citations
TL;DR: This survey presents a rigorous review of the different applications of fuzzy logic in opinion mining and summarizes over one hundred and twenty articles published in the past decade regarding tasks and applications of opinion mining.
Abstract: The advent of Web 2.0 and its continuous growth has yielded enormous amounts of freely available user-generated information. Within this information, it is easy to find subjective texts, especially on social networks and eCommerce platforms that contain valuable information about users. Consequently, the field of opinion mining has attracted considerable interest over the last decade. Many new research articles are published every day, in which different artificial intelligence techniques (e.g., neural networks, fuzzy logic, clustering algorithms, and evolving computing) are applied to various tasks and applications related to opinion mining. Given this context, this survey presents a rigorous review of the different applications of fuzzy logic in opinion mining. The review portrays different uses of fuzzy logic and summarizes over one hundred and twenty articles published in the past decade regarding tasks and applications of opinion mining. This study is organized around three primary tasks, feature processing, review classification and emotions and also pays special attention to sentiment analysis applications whose core technique uses fuzzy logic to achieve the stated goals.
45 citations
TL;DR: In this article , a graph convolutional network with multiple weight mechanisms is proposed for aspect-based sentiment analysis, where a dynamic weight alignment mechanism is proposed to encourage the model to make full use of BERT.
16 citations
TL;DR: The research results show that the proposed convolution kernel has certain effects and can provide theoretical reference for subsequent related research and verify the performance of the algorithm.
Abstract: Image classification method is currently the more popular image technology, but it still has certain problems in practice. In order to improve the image classification effect, this study proposes a new convolution kernel, which can effectively detect the corresponding features with different transformations by actively transforming the relative positions of the connections in the convolution kernel. Moreover, in a network, replacing a traditional convolution kernel with a complex convolution kernel can significantly improve network performance. In order to verify the performance of the image classification method proposed in this study, the performance comparison of the algorithm was performed by setting a control experiment. The research results show that the proposed method has certain effects and can provide theoretical reference for subsequent related research.
13 citations
TL;DR: Wang et al. as discussed by the authors designed and implemented the COVID-19 public opinion monitoring system based on time series thermal new word mining and a new word structure discovery scheme based on the timing explosion of network topics and a Chinese sentiment analysis method.
Abstract: With the spread and development of new epidemics, it is of great reference value to identify the changing trends of epidemics in public emotions We designed and implemented the COVID-19 public opinion monitoring system based on time series thermal new word mining A new word structure discovery scheme based on the timing explosion of network topics and a Chinese sentiment analysis method for the COVID-19 public opinion environment are proposed Establish a "Scrapy-Redis-Bloomfilter" distributed crawler framework to collect data The system can judge the positive and negative emotions of the reviewer based on the comments, and can also reflect the depth of the seven emotions such as Hopeful, Happy, and Depressed Finally, we improved the sentiment discriminant model of this system and compared the sentiment discriminant error of COVID-19 related comments with the Jiagu deep learning model The results show that our model has better generalization ability and smaller discriminant error We designed a large data visualization screen, which can clearly show the trend of public emotions, the proportion of various emotion categories, keywords, hot topics, etc , and fully and intuitively reflect the development of public opinion
13 citations
References
More filters
01 Oct 2014
TL;DR: A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure.
Abstract: Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition.
30,558 citations
"Constructing domain-dependent senti..." refers background in this paper
...Traditional word representation models (e.g., Word2vec [23], Glove [29]) are context independent which map words that often co-occur in a context to points that are close by in the embedding space....
[...]
..., Word2vec [23], Glove [29]) are context independent which map words that often co-occur in a context to points that are close by in the embedding space....
[...]
Posted Content•
TL;DR: A new language representation model, BERT, designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.
Abstract: We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications.
BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).
29,480 citations
"Constructing domain-dependent senti..." refers background or methods in this paper
...Recent work suggests that task-specific architectures are no longer necessary and transferring many self-attention blocks is sufficient (e.g., BERT [8], ELMo [31], OpenAI GPT [35]), which takes the word position into consideration using transformer network....
[...]
..., the recent pre-trained fine-tuning models [8, 35] used transformer for this purpose) reflects on some irregular words position in the sentence....
[...]
..., BERT [8], ELMo [31], OpenAI GPT [35]), which takes the word position into consideration using transformer network....
[...]
Book•
08 Jul 2008TL;DR: This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems and focuses on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis.
Abstract: An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.
This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.
7,452 citations
"Constructing domain-dependent senti..." refers methods in this paper
...As, in sentence level, an overall opinion is expressed in the whole sentence, we thus leverage rule-based method and the state-of-the-art lexicon-based classifier to infer the sentiment polarity, while the sentence, in aspect level, may express various opinions toward different aspects [20, 28, 33]....
[...]
15 Feb 2018
TL;DR: This paper introduced a new type of deep contextualized word representation that models both complex characteristics of word use (e.g., syntax and semantics), and how these uses vary across linguistic contexts (i.e., to model polysemy).
Abstract: We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals.
7,412 citations
22 Aug 2004
TL;DR: This research aims to mine and to summarize all the customer reviews of a product, and proposes several novel techniques to perform these tasks.
Abstract: Merchants selling products on the Web often ask their customers to review the products that they have purchased and the associated services. As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. For a popular product, the number of reviews can be in hundreds or even thousands. This makes it difficult for a potential customer to read them to make an informed decision on whether to purchase the product. It also makes it difficult for the manufacturer of the product to keep track and to manage customer opinions. For the manufacturer, there are additional difficulties because many merchant sites may sell the same product and the manufacturer normally produces many kinds of products. In this research, we aim to mine and to summarize all the customer reviews of a product. This summarization task is different from traditional text summarization because we only mine the features of the product on which the customers have expressed their opinions and whether the opinions are positive or negative. We do not summarize the reviews by selecting a subset or rewrite some of the original sentences from the reviews to capture the main points as in the classic text summarization. Our task is performed in three steps: (1) mining product features that have been commented on by customers; (2) identifying opinion sentences in each review and deciding whether each opinion sentence is positive or negative; (3) summarizing the results. This paper proposes several novel techniques to perform these tasks. Our experimental results using reviews of a number of products sold online demonstrate the effectiveness of the techniques.
7,330 citations
"Constructing domain-dependent senti..." refers background or methods in this paper
...Since we do not have access to labeled data, in the training step, that can accurately tackle such issue, we thus used the sentiment list generated in [16, 46], i....
[...]
...2, we used the lexicon introduced in [16] to drop out the non-potential sentiment words...
[...]
...SentiWordNet [2] is built based on the glosses associated with synsets and on vectorial term representations for semi-supervised synset classification....
[...]
..., lexicon generated in [16]) by SentiDomain and the compared state-ofthe-art lexicons alternatives....
[...]
...Some approaches proposed to begin with a small list (i.e., manually labeled) and extend the list based on some features (e.g., frequent features and semantic orientation) with the help of prior knowledge from WordNet [16]....
[...]