Feature-Based Opinion Mining Approach (FOMA) for Improved Book Recommendation

Sohail, Shahab Saquib; Siddiqui, Jamshed; Ali, Rashid

doi:10.1007/s13369-018-3282-3

Cited by 23 publications

(13 citation statements)

References 26 publications

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“…The feature-based methods, as their name indicate, extract the features of users and/or items for the machine learning process [14]. Sohail et al [15] present an opinion miningbased recommendation technique to provide the university students with promising books for their syllabus; however, this technique could be subjectively biased. Uyangoda et al [16] apply a user-profile-feature-based approach to improve the recommender system with few user records.…”

Section: Knowledge Graph-based Recommendationmentioning

confidence: 99%

Resource Recommendation Based on Industrial Knowledge Graph in Low-Resource Conditions

Liu

et al. 2022

Int J Comput Intell Syst

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Resource recommendation is extremely challenging under low-resource conditions because representation learning models require sufficient triplets for their training, and the presence of massive long-tail resources leads to data sparsity and cold-start problems. In this paper, an industrial knowledge graph is developed to integrate resources for manufacturing enterprises, and we further formulate long-tail recommendations as a few-shot relational learning problem of learning-to-recommend resources with few interactions under low-resource conditions. First, an industrial knowledge graph is constructed based on the predesigned resource schema. Second, we conduct schema-based reasoning on the schema to heuristically complete the knowledge graph. At last, we propose a multi-head attention-based meta relational learning model with schema-based reasoning to recommend long-tail resources under low-resource conditions. With the IN-Train setting, 5-shot experimental results on the NELL-One and Wiki-One datasets achieve average improvements of 28.8 and 13.3% respectively, compared with MetaR. Empirically, the attention mechanism with relation space translation learns the most important relations for fast convergence. The proposed graph-based platform specifies how to recommend resources using the industrial knowledge graph under low-resource conditions.

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Section: Knowledge Graph-based Recommendationmentioning

confidence: 99%

Resource Recommendation Based on Industrial Knowledge Graph in Low-Resource Conditions

Liu

et al. 2022

Int J Comput Intell Syst

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“…A feature-based opinion mining technique is proposed in [14] to recommend books for university syllabus. The authors used the features presented in [12] and developed the technique presented in [9].…”

Section: Related Workmentioning

confidence: 99%

Ontology-Based Approach for Feature Level Sentiment Analysis

Aboelela

Gad

Ismail

2021

IJICIS

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Through the state-of-the-art digitalization, we can see a massive growth in user-generated content on the web that provides feedback from people on a variety of topics. However, manually managing large-scale user feedback would be a difficult task and a waste of time. Therefore, the concept of sentiment analysis is emerged. Sentiment analysis is a computerized study of individuals' feelings and opinions about an entity or product. It can be executed at three different levels: document level, sentence or phrase level, and feature level. This paper proposes a novel ontologybased approach for feature level sentiment analysis. The proposed approach extracts the product features using semantic similarity and Wordnet ontology and uses the SentiWordent dictionary to classify the users' comments as positive and negative. Furthermore, it manages negative words to obtain more precise classification results. The proposed approach is assessed by using two benchmark amazon products' datasets in terms of accuracy; recall, precision, and f-measure. The performance reaches to 92.4% accuracy, 97.2% precision, 92.8 % recall, and 94.4% f-measure.

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“…Similarly, most frequent users and most frequent words, which have been stored with the tweets as their features, are shown in figure 3, figure 4, figure 4 and figure 6. To calculate the modified spearman rank-order correlation coefficient [44], following (1) is used.…”

Section: Experimental Analysismentioning

confidence: 99%

An Analysis of Twitter Users From The Perspective of Their Behavior, Language, Region and Development Indices -- A Study of 80 Million Tweets

Sohail¹,

Khan²,

Alam³

2021

Preprint

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The need for a comprehensive study to explore various aspects of online social media has been instigated by many researchers. This paper gives an insight into the social platform, Twitter. In this present work, we have illustrated stepwise procedure for crawling the data and discuss the key issues related to extracting associated features that can be useful in Twitter-related research while crawling these data from Application Programming Interfaces (APIs). Further, the data that comprises of over 86 million tweets have been analysed from various perspective including the most used languages, most frequent words, most frequent users, countries with most and least tweets and re-tweets, etc. The analysis reveals that the users' data associated with Twitter has a high affinity for researches in the various domain that includes politics, social science, economics, and linguistics, etc. In addition, the relation between Twitter users of a country and its human development index has been identified. It is observed that countries with very high human development indices have a relatively higher number of tweets compared to low human development indices countries. It is envisaged that the present study shall open many doors of researches in information processing and data science.

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Feature-Based Opinion Mining Approach (FOMA) for Improved Book Recommendation

Cited by 23 publications

References 26 publications

Resource Recommendation Based on Industrial Knowledge Graph in Low-Resource Conditions

Resource Recommendation Based on Industrial Knowledge Graph in Low-Resource Conditions

Ontology-Based Approach for Feature Level Sentiment Analysis

An Analysis of Twitter Users From The Perspective of Their Behavior, Language, Region and Development Indices -- A Study of 80 Million Tweets

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