507 research outputs found
Sentiment analysis for e-commerce product reviews in Chinese based on sentiment lexicon and deep learning
In recent years, with the rapid development of Internet technology, online shopping has become a mainstream way for users to purchase and consume. Sentiment analysis of a large number of user reviews on e-commerce platforms can effectively improve user satisfaction. This paper proposes a new sentiment analysis model-SLCABG, which is based on the sentiment lexicon and combines Convolutional Neural Network (CNN) and attention-based Bidirectional Gated Recurrent Unit (BiGRU). In terms of methods, the SLCABG model combines the advantages of sentiment lexicon and deep learning technology, and overcomes the shortcomings of existing sentiment analysis model of product reviews. The SLCABG model combines the advantages of the sentiment lexicon and deep learning techniques. First, the sentiment lexicon is used to enhance the sentiment features in the reviews. Then the CNN and the Gated Recurrent Unit (GRU) network are used to extract the main sentiment features and context features in the reviews and use the attention mechanism to weight. And finally classify the weighted sentiment features. In terms of data, this paper crawls and cleans the real book evaluation of dangdang.com, a famous Chinese e-commerce website, for training and testing, all of which are based on Chinese. The scale of the data has reached 100000 orders of magnitude, which can be widely used in the field of Chinese sentiment analysis. The experimental results show that the model can effectively improve the performance of text sentiment analysis
Natural Language Processing in-and-for Design Research
We review the scholarly contributions that utilise Natural Language
Processing (NLP) methods to support the design process. Using a heuristic
approach, we collected 223 articles published in 32 journals and within the
period 1991-present. We present state-of-the-art NLP in-and-for design research
by reviewing these articles according to the type of natural language text
sources: internal reports, design concepts, discourse transcripts, technical
publications, consumer opinions, and others. Upon summarizing and identifying
the gaps in these contributions, we utilise an existing design innovation
framework to identify the applications that are currently being supported by
NLP. We then propose a few methodological and theoretical directions for future
NLP in-and-for design research
A Survey on Semantic Processing Techniques
Semantic processing is a fundamental research domain in computational
linguistics. In the era of powerful pre-trained language models and large
language models, the advancement of research in this domain appears to be
decelerating. However, the study of semantics is multi-dimensional in
linguistics. The research depth and breadth of computational semantic
processing can be largely improved with new technologies. In this survey, we
analyzed five semantic processing tasks, e.g., word sense disambiguation,
anaphora resolution, named entity recognition, concept extraction, and
subjectivity detection. We study relevant theoretical research in these fields,
advanced methods, and downstream applications. We connect the surveyed tasks
with downstream applications because this may inspire future scholars to fuse
these low-level semantic processing tasks with high-level natural language
processing tasks. The review of theoretical research may also inspire new tasks
and technologies in the semantic processing domain. Finally, we compare the
different semantic processing techniques and summarize their technical trends,
application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN
1566-2535. The equal contribution mark is missed in the published version due
to the publication policies. Please contact Prof. Erik Cambria for detail
Multimodal sentiment analysis with image-text interaction network
More and more users are getting used to posting images and text on social networks to share their emotions or opinions. Accordingly, multimodal sentiment analysis has become a research topic of increasing interest in recent years. Typically, there exist affective regions that evoke human sentiment in an image, which are usually manifested by corresponding words in peoples comments. Similarly, people also tend to portray the affective regions of an image when composing image descriptions. As a result, the relationship between image affective regions and the associated text is of great significance for multimodal sentiment analysis. However, most of the existing multimodal sentiment analysis approaches simply concatenate features from image and text, which could not fully explore the interaction between them, leading to suboptimal results. Motivated by this observation, we propose a new image-text interaction network (ITIN) to investigate the relationship between affective image regions and text for multimodal sentiment analysis. Specifically, we introduce a cross-modal alignment module to capture region-word correspondence, based on which multimodal features are fused through an adaptive cross-modal gating module. Moreover, considering the complementary role of context information on sentiment analysis, we integrate the individual-modal contextual feature representations for achieving more reliable prediction. Extensive experimental results and comparisons on public datasets demonstrate that the proposed model is superior to the state-of-the-art methods
Subjectivity Analysis In Opinion Mining - A Systematic Literature Review
Subjectivity analysis determines existence of subjectivity in text using subjective clues.It is the first task in opinion mining process.The difference between subjectivity analysis and polarity determination is the latter process subjective text to determine the orientation as positive or negative.There were many techniques used to solve the problem of segregating subjective and objective text.This paper used systematic literature review (SLR) to compile the undertaking study in subjective analysis.SLR is a literature review that collects multiple and critically analyse multiple studies to answer the research questions.Eight research questions were drawn for this purpose.Information such as technique,corpus,subjective clues representation and performance were extracted from 97 articles known as primary studies.This information was analysed to identify the strengths and weaknesses of the technique,affecting
elements to the performance and missing elements from the subjectivity analysis.The SLR has found that majority of the study are using machine learning approach to identify and learn subjective text due to the nature of subjectivity analysis problem that is viewed as classification problem.The performance of this approach outperformed other approaches though currently it is at satisfactory level.Therefore,more studies are needed to improve the performance of subjectivity analysis
Text-based Sentiment Analysis and Music Emotion Recognition
Nowadays, with the expansion of social media, large amounts of user-generated
texts like tweets, blog posts or product reviews are shared online. Sentiment polarity
analysis of such texts has become highly attractive and is utilized in recommender
systems, market predictions, business intelligence and more. We also witness deep
learning techniques becoming top performers on those types of tasks. There are
however several problems that need to be solved for efficient use of deep neural
networks on text mining and text polarity analysis.
First of all, deep neural networks are data hungry. They need to be fed with
datasets that are big in size, cleaned and preprocessed as well as properly labeled.
Second, the modern natural language processing concept of word embeddings as a
dense and distributed text feature representation solves sparsity and dimensionality
problems of the traditional bag-of-words model. Still, there are various uncertainties
regarding the use of word vectors: should they be generated from the same dataset
that is used to train the model or it is better to source them from big and popular
collections that work as generic text feature representations? Third, it is not easy for
practitioners to find a simple and highly effective deep learning setup for various
document lengths and types. Recurrent neural networks are weak with longer texts
and optimal convolution-pooling combinations are not easily conceived. It is thus
convenient to have generic neural network architectures that are effective and can
adapt to various texts, encapsulating much of design complexity.
This thesis addresses the above problems to provide methodological and practical
insights for utilizing neural networks on sentiment analysis of texts and achieving
state of the art results. Regarding the first problem, the effectiveness of various
crowdsourcing alternatives is explored and two medium-sized and emotion-labeled
song datasets are created utilizing social tags. One of the research interests of Telecom
Italia was the exploration of relations between music emotional stimulation and
driving style. Consequently, a context-aware music recommender system that aims
to enhance driving comfort and safety was also designed. To address the second
problem, a series of experiments with large text collections of various contents and
domains were conducted. Word embeddings of different parameters were exercised
and results revealed that their quality is influenced (mostly but not only) by the
size of texts they were created from. When working with small text datasets, it is
thus important to source word features from popular and generic word embedding
collections. Regarding the third problem, a series of experiments involving convolutional
and max-pooling neural layers were conducted. Various patterns relating
text properties and network parameters with optimal classification accuracy were
observed. Combining convolutions of words, bigrams, and trigrams with regional
max-pooling layers in a couple of stacks produced the best results. The derived
architecture achieves competitive performance on sentiment polarity analysis of
movie, business and product reviews.
Given that labeled data are becoming the bottleneck of the current deep learning
systems, a future research direction could be the exploration of various data programming
possibilities for constructing even bigger labeled datasets. Investigation
of feature-level or decision-level ensemble techniques in the context of deep neural
networks could also be fruitful. Different feature types do usually represent complementary
characteristics of data. Combining word embedding and traditional text
features or utilizing recurrent networks on document splits and then aggregating the
predictions could further increase prediction accuracy of such models
Using sentiment analysis in tourism research: A systematic, bibliometric, and integrative review
Purpose: Sentiment analysis is built from the information provided through text (reviews) to help understand the social sentiment toward their brand, product, or service. The main purpose of this paper is to draw an overview of the topics and the use of the sentiment analysis approach in tourism research.
Methods: The study is a bibliometric analysis (VOSviewer), with a systematic and integrative review. The search occurred in March 2021 (Scopus) applying the search terms "sentiment analysis" and "tourism" in the title, abstract, or keywords, resulting in a final sample of 111 papers.
Results: This analysis pointed out that China (35) and the United States (24) are the leading countries studying sentiment analysis with tourism. The first paper using sentiment analysis was published in 2012; there is a growing interest in this topic, presenting qualitative and quantitative approaches. The main results present four clusters to understand this subject. Cluster 1 discusses sentiment analysis and its application in tourism research, searching how online reviews can impact decision-making. Cluster 2 examines the resources used to make sentiment analysis, such as social media. Cluster 3 argues about methodological approaches in sentiment analysis and tourism, such as deep learning and sentiment classification, to understand the user-generated content. Cluster 4 highlights questions relating to the internet and tourism.
Implications: The use of sentiment analysis in tourism research shows that government and entrepreneurship can draw and enhance communication strategies, reduce cost, and time, and mainly contribute to the decision-making process and understand consumer behavior
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