Knowledge-Guided Sentiment Analysis Via Learning From Natural Language Explanations

Ke, Zunwang; Sheng, Jiabao; Li, Zhe; Silamu, Wushour; Guo, Qinglang

doi:10.1109/access.2020.3048088

Cited by 28 publications

(9 citation statements)

References 34 publications

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“…Analisis sentimen merupakan salah satu teknik Natural Language Processing (NLP) yang menganalisis pendapat, sikap, dan emosi terhadap suatu entitas yang berupa teks. Analisis sentimen diperlukan sebagai bahan evaluasi yang selanjutnya menjadi dasar dalam pengambilan keputusan [3]. Kesulitan dalam analisis sentimen biasanya terjadi karena terlalu banyaknya data.…”

Section: Pendahuluanunclassified

Sentimen Ulasan Destinasi Wisata Pulau Bali Menggunakan Bidirectional Long Short Term Memory

Af'idah

Dairoh

Handayani

et al. 2022

matrik

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Pemerintah dan pelaku industri pariwisata mengalami permasalahan dalam menentukan prioritas pengembangan suatu destinasi wisata. Karena itu, diperlukan identifikasi objek wisata yang diminati namun banyak mendapat ulasan buruk melalui ulasan dari masyarakat yang tersebar di internet. Penelitian ini bertujuan melakukan analisis sentimen terhadap ulasan objek wisata di Pulau Bali menggunakan Bi-LSTM dan Word2Vec, sehingga diperoleh model terbaik yang dapat digunakan untuk mengidentifikasi objek wisata potensial namun mendapat ulasan buruk. Bi-LSTM merupakan deep learning yang menawarkan akurasi yang lebih baik daripada LSTM biasa. Sedangkan Word2Vec merupakan pretraining yang dipilih karena dapat menangkap makna semantik teks. Penelitian ini menggunakan data ulasan objek wisata di Pulau Bali yang berasal dari situs tripadvisor.com. Penelitian dimulai dari pengumpulan data, perancangan alur program, preprocessing, pretraining Word2Vec, pembagian data uji dan data latih, pelatihan dan pengujian, serta evaluasi penentuan model terbaik. Akurasi terbaik dihasilkan oleh kombisasi Word2Vec terdiri dari CBOW, Hierarchical Softmax, dimensi 200, Bi-LSTM dengan dropout sebesar 0,5 dan learning rate sebesar 0,0001. Kombinasi tersebut menghasilkan akurasi tertinggi dari keseluruhan 108 kombinasi yaitu sebesar 96,86%, precission sebesar 96,53%, Recall sebesar 96,31%, F1 Measure sebesar 96,41%. Akurasi yang baik tersebut membuktikan bahwa kombinasi parameter Bi-LSTM dan Word2Vec cocok digunakan untuk analisis sentimen ulasan objek wisata di Pulau Bali.

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Section: Pendahuluanunclassified

Sentimen Ulasan Destinasi Wisata Pulau Bali Menggunakan Bidirectional Long Short Term Memory

Af'idah

Dairoh

Handayani

et al. 2022

matrik

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“…SentiView [1], a vocabulary-based approach for sentiment inquiry, has been presented as a second framework for sentiment investigation. Because of preprocessing and the exclusion of non-opinion tweets from the data, they were able to achieve excellent accuracy.…”

Section: Literature Surveymentioning

confidence: 99%

Sentiment Analysis on Social Network Data and Its Marketing Strategies: A Review

Dash¹,

Mishra²,

Dara³

2022

ECS Trans.

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Any social media plan should include the creation of sticky content. Marketers produce viral content in the expectation that it will go viral rapidly. The main issue is that influencer marketing efforts are difficult to track and might have catastrophic ramifications. Sentiment analysis may be used to assess influencer marketing efforts and assist brands in making educated decisions. The goal of the study is to determine how effective an influencer is at creating or boosting intangible assets, as well as to provide practical data for brands looking to hire the ideal influencer for their products. The nature of the research study is conceptual. The researcher analyzed and drew conclusions using secondary data from reliable secondary sources and conceptual demonstration. The next consistent promoting field is social media. Currently, Facebook dominates the advanced advertising area, closely followed by Twitter. Despite the evident benefits that these platforms provide, sites such as YouTube and MySpace are less popular. We investigate the effects of various internet promotional efforts on brand awareness. The purpose of this research is to see how social media sentimental analysis affects business growth. To investigate the audience's reaction to the brand in order to develop a fresh marketing strategy. To investigate the impact of a social media campaign on the target audience. With knowledge of the public's opinion toward a product or service, one can decide to buy. By processing and analyzing public sentiment received from internet reviews and social media, the polarity of the sentiment can be determined.

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“…The second type uses traditional machine learning algorithms to extract features from sentences and evaluate the classification performance of models based on these features. This method has a faster running speed, can make full use of corpus information, but is limited by the human effort required to obtain feature information and its narrow application scope [3] . The third type extracts a large number of feature information from experimental samples using deep learning techniques, learns the deep intrinsic information of sample data, saves the trouble of manually selecting features, and has better adaptability.…”

Section: Introductionmentioning

confidence: 99%

Sentiment analysis using multi neighborhood representation learning embeddings: graph convolutional neural networks

Lv,

Liu

2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

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With the thriving of social networks, sentiment analysis at aspect-level has become a research hotspot in the field of natural language processing, aiming to extract user expressed sentiment from massive text data. To tackle the problem that current sentiment analysis methods cannot simultaneously consider the contextual and syntactic information of aspect terms, this paper proposes a targeted model for aspect-level sentiment analysis by embedding different neighborhood representations. By integrating aspect neighborhood information and node neighborhood information through convolutional neural networks, our proposed model achieves high effectiveness on five public datasets as demonstrated by the evaluation coefficients constructed in our study.

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Knowledge-Guided Sentiment Analysis Via Learning From Natural Language Explanations

Cited by 28 publications

References 34 publications

Sentimen Ulasan Destinasi Wisata Pulau Bali Menggunakan Bidirectional Long Short Term Memory

Sentimen Ulasan Destinasi Wisata Pulau Bali Menggunakan Bidirectional Long Short Term Memory

Sentiment Analysis on Social Network Data and Its Marketing Strategies: A Review

Sentiment analysis using multi neighborhood representation learning embeddings: graph convolutional neural networks

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