A context-aware embeddings supported method to extract a fuzzy sentiment polarity dictionary

Bernabé-Moreno, Juan; Tejeda-Lorente, Álvaro; Herce-Zelaya, Julio; Porcel, Carlos; Herrera‐Viedma, Enrique

doi:10.1016/j.knosys.2019.105236

Cited by 31 publications

(15 citation statements)

References 24 publications

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“…To create this dictionary, the first small set of sentiment words, possibly with very short contexts like negations, is collected along with its polarity labels (Bernabé-Moreno et al. 2020 ). The dictionary is then updated by looking for their synonymous (words with the same polarity) and antonymous (words with opposite polarity).…”

Section: Process Of Sentiment Analysis and Emotion Detectionmentioning

confidence: 99%

A review on sentiment analysis and emotion detection from text

Nandwani

Verma

2021

Soc. Netw. Anal. Min.

354

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Social networking platforms have become an essential means for communicating feelings to the entire world due to rapid expansion in the Internet era. Several people use textual content, pictures, audio, and video to express their feelings or viewpoints. Text communication via Web-based networking media, on the other hand, is somewhat overwhelming. Every second, a massive amount of unstructured data is generated on the Internet due to social media platforms. The data must be processed as rapidly as generated to comprehend human psychology, and it can be accomplished using sentiment analysis, which recognizes polarity in texts. It assesses whether the author has a negative, positive, or neutral attitude toward an item, administration, individual, or location. In some applications, sentiment analysis is insufficient and hence requires emotion detection, which determines an individual’s emotional/mental state precisely. This review paper provides understanding into levels of sentiment analysis, various emotion models, and the process of sentiment analysis and emotion detection from text. Finally, this paper discusses the challenges faced during sentiment and emotion analysis.

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Section: Process Of Sentiment Analysis and Emotion Detectionmentioning

confidence: 99%

A review on sentiment analysis and emotion detection from text

Nandwani

Verma

2021

Soc. Netw. Anal. Min.

354

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“…For example, Wu and Chang (2020) used a dictionary-based approach to identify the sentiment polarity of online shoppers' posts. However, the accuracy of this approach depends on the quality of the constructed sentiment dictionary, which highlights the existence of the following flaws (Bernabe-Moreno et al , 2020). At present, there is no general dictionary for the logistics field, and the establishment of the dictionary requires considerable manpower and material resources.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring customer satisfaction in cold chain logistics using a text mining approach

Lim¹,

Song

2021

IMDS

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PurposeWith the fierce competition in the cold chain logistics market, achieving and maintaining excellent customer satisfaction is the key to an enterprise's ability to stand out. This research aims to determine the factors that affect customer satisfaction in cold chain logistics, which helps cold chain logistics enterprises identify the main aspects of the problem. Further, the suggestions are provided for cold chain logistics enterprises to improve customer satisfaction.Design/methodology/approachThis research uses the text mining approach, including topic modeling and sentiment analysis, to analyze the information implicit in customer-generated reviews. First, latent Dirichlet allocation (LDA) model is used to identify the topics that customers focus on. Furthermore, to explore the sentiment polarity of different topics, bi-directional long short-term memory (Bi-LSTM), a type of deep learning model, is adopted to quantify the sentiment score. Last, regression analysis is performed to identify the significant factors that affect positive, neutral and negative sentiment.FindingsThe results show that eight topics that customer focus are determined, namely, speed, price, cold chain transportation, package, quality, error handling, service staff and logistics information. Among them, speed, price, transportation and product quality significantly affect customer positive sentiment, and error handling and service staff are significant factors affecting customer neutral and negative sentiment, respectively.Research limitations/implicationsThe data of the customer-generated reviews in this research are in Chinese. In the future, multi-lingual research can be conducted to obtain more comprehensive insights.Originality/valuePrior studies on customer satisfaction in cold chain logistics predominantly used questionnaire method, and the disadvantage of which is that interviewees may fill out the questionnaire arbitrarily, which leads to inaccurate data. For this reason, it is more scientific to discover customer satisfaction from real behavioral data. In response, customer-generated reviews that reflect true emotions are used as the data source for this research.

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“…Taking music recommendation as an example, the music embedded item v ∈ R n may combine the single hot identification [11], attribute, word package or context information [22] of the music item v. Given the set of 1-hop ripples M 1 u of the embedded term v and the user u, each triple…”

Section: Figurementioning

confidence: 99%

Recommendation Algorithm Based on Knowledge Graph to Propagate User Preference

Yang¹,

Cheng²

2021

IJCIS

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In recommendation algorithms, data sparsity and cold start problems are inevitable. To solve such problems, researchers apply auxiliary information to recommendation algorithms, mine users' historical records to obtain more potential information, and then improve recommendation performance. In this paper, ST_RippleNet, a model that combines knowledge graphs with deep learning, is proposed. This model starts by building the required knowledge graph. Then, the potential interest of users is mined through the knowledge graph, which stimulates the spread of users' preferences on the set of knowledge entities. In preference propagation, we use a triple multi-layer attention mechanism to obtain triple information through the knowledge graph and use the user preference distribution for candidate items formed by users' historical click information to predict the final click probability. Using ST_RippleNet model can better obtain triple information in knowledge graph and mine more useful information. In the ST_RippleNet model, the music data set is added to the movie and book data set; additionally, an improved loss function is used in the model, which is optimized by the RMSProp optimizer. Finally, the tanh function is added to predict the click probability to improve recommendation performance. Compared with current mainstream recommendation methods, ST_RippleNet achieves very good performance in terms of the area under the curve (AUC) and accuracy (ACC) and substantially improves movie, book and music recommendations.

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A context-aware embeddings supported method to extract a fuzzy sentiment polarity dictionary

Cited by 31 publications

References 24 publications

A review on sentiment analysis and emotion detection from text

A review on sentiment analysis and emotion detection from text

Exploring customer satisfaction in cold chain logistics using a text mining approach

Recommendation Algorithm Based on Knowledge Graph to Propagate User Preference

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