Improving collaborative filtering using lexicon-based sentiment analysis

Sallam, Rouhia M.; Hussein, Mansour F.; Mousa, Hamdy M.

doi:10.11591/ijece.v12i2.pp1744-1753

Cited by 10 publications

(6 citation statements)

References 22 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1) Lexicon-based [7] 2) Machine learning [8] The first approach uses a set of words that carry the sentiments of the opinion holder and the sentiment in the text is computed from those sets of words, which are identified in the text during the process of analysis. The second approach uses a model created from a large set of sentiment-labeled texts; then it is applied to the stream of unlabeled text documents.…”

Section: Literature Reviewmentioning

confidence: 99%

Development and Evaluation of Gold Standard Dataset for Sentiment Analysis of Tweets

Ahmed,

Hina,

Asif

et al. 2024

PakJET

View full text Add to dashboard Cite

Pre-labeled data is typically required for supervised machine learning. A limited number of object classes in the majority of open access and pre-annotated datasets make them unsuitable for certain tasks, even though they are readily available for training machine learning algorithms. For custom models, previously available pre-annotated data is typically insufficient, so gathering and preparing training data is necessary for the majority of real-world applications. The quantity and quality of annotations clearly trade-off with one another. Either more annotated data can be produced or better data quality can be guaranteed by allocating time and resources. Development of the gold standard by annotating textual information is an essential part of the “Text Analytics” domain in the field of “Natural Language Processing-NLP”. In “Text Analytics”, annotation can be done by adopting a manual, semi-automatic or automatic approach. In the case of the manual approach, annotators often work with partial parts of the corpus, and the results are generalized by automated text classification which may affect the final classification results. Annotations reliability and suitability of assigned labels are particularly important in the NLP applications related to opinion mining or sentiment analysis. In this research study, we have evaluated the significance of the annotation process on a novel dataset that contained multiple languages (English, Roman Urdu), a free text dataset that was extracted from Twitter. This unique dataset contained multiple languages which makes this annotation process essential for researching this data. Using this multi-language dataset, we examine the inter-annotator agreement in multiclass and multi-label sentiment annotation. To scrutinize the reliability of this research work, several annotation agreement metrics, statistical analysis, and Machine Learning methods have been considered to evaluate the accuracy of resulting annotations. It was observed that the annotation process is significant and a complex step that is essential for the proper implementation of Natural Language processing tasks for text analytics in machine learning. During this research, different gaps were identified and resolved which can impact the overall reliability of the annotation process which are reported in this paper. We conclude that while inaccurate annotations worsen the results, the impact is minimal, at least when using text data. The advantages of the larger annotated data set (obtained by employing subpar auto-annotation techniques) surpass the degradation resulting from the use of annotated data.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Development and Evaluation of Gold Standard Dataset for Sentiment Analysis of Tweets

Ahmed,

Hina,

Asif

et al. 2024

PakJET

View full text Add to dashboard Cite

show abstract

“…In a different approach, authors in [27] adopted a novel method to enhance the effectiveness of collaborative filtering algorithms. They utilized lexicon-based sentiment analysis to incorporate the emotional content present in the recommended items, resulting in a more precise prediction of users' preferences.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis

Karabila,

Darraz,

El-Ansari

et al. 2023

Future Internet

View full text Add to dashboard Cite

Recommendation systems (RSs) are widely used in e-commerce to improve conversion rates by aligning product offerings with customer preferences and interests. While traditional RSs rely solely on numerical ratings to generate recommendations, these ratings alone may not be sufficient to offer personalized and accurate suggestions. To overcome this limitation, additional sources of information, such as reviews, can be utilized. However, analyzing and understanding the information contained within reviews, which are often unstructured data, is a challenging task. To address this issue, sentiment analysis (SA) has attracted considerable attention as a tool to better comprehend a user’s opinions, emotions, and attitudes. In this study, we propose a novel RS that leverages ensemble learning by integrating sentiment analysis of textual data with collaborative filtering techniques to provide users with more precise and individualized recommendations. Our system was developed in three main steps. Firstly, we used unsupervised “GloVe” vectorization for better classification performance and built a sentiment model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Secondly, we developed a recommendation model based on collaborative filtering techniques. Lastly, we integrated our sentiment analysis model into the RS. Our proposed model of SA achieved an accuracy score of 93%, which is superior to other models. The results of our study indicate that our approach enhances the accuracy of the recommendation system. Overall, our proposed system offers customers a more reliable and personalized recommendation service in e-commerce.

show abstract

“…Clustering algorithms bear a resemblance to user-based or item-based collaborative filtering since clustering of users or items is executed based on the distance metric values [10]. In user-based cluster-ing, users can be segregated into various target groups based on metric values, and items with higher scores in the same target group can be recommended to the target user, while item-based clustering is akin to item-based collaborative filtering [11].…”

Section: Related Workmentioning

confidence: 99%

Analysis of Advertising Promotion Strategy Based on Improved Collaborative Filtering Algorithm under Digital Media Technology

2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

View full text Add to dashboard Cite

With the advent of the Internet, individuals have access to an ever-increasing array of external information sources, and as such, advertising information has undergone a significant transformation. In this study, we present a highly accurate, high-throughput, and high-yield ad recommendation system that is capable of precisely targeting users for personalized ad recommendations. Our approach involves enhancing the user interest preference model, introducing ad keywords as labels into the similarity calculation of Query pages, and employing a weighted integrated similarity measure of Query pages to mitigate sparsity within the similarity matrix. Furthermore, we optimize the system process of ad promotion and devise an ad recommendation strategy based on multiple parallel recall and uniform sorting distribution. Our model training results indicate that the proposed algorithm elevates Precision, Recall, and F1 by 27%, 25%, and 28%, respectively, compared to the traditional model. Additionally, our system test results demonstrate enhanced scalability, with approximately four times higher concurrent performance, as well as improvements in high expansion, low latency, and strong stability. Such results hold particular significance in guiding the design of actual ad recommendation system projects.

show abstract

Improving collaborative filtering using lexicon-based sentiment analysis

Cited by 10 publications

References 22 publications

Development and Evaluation of Gold Standard Dataset for Sentiment Analysis of Tweets

Development and Evaluation of Gold Standard Dataset for Sentiment Analysis of Tweets

Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis

Analysis of Advertising Promotion Strategy Based on Improved Collaborative Filtering Algorithm under Digital Media Technology

Contact Info

Product

Resources

About