Comparison of Word2vec and Doc2vec Methods for Text Classification of Product Reviews

Hendrawan, Ivan Rifky; Utami, Ema; Hartanto, Anggit Dwi

doi:10.1109/icitisee57756.2022.10057702

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…The number of words in the corpus multiplied by the number of hidden neurons in the hidden layer determines the dimensions of the weight matrix in each layer [25]. Words are converted to vectors using the weight matrix located in the drilled hidden layer.…”

Section: Word2vecmentioning

confidence: 99%

Combining Bi-LSTM And Word2vec Embedding For Sentiment Analysis Models Of Application User Reviews

Verra Budhi Lestari,

Ema Utami,

Hanafi

2024

ijcs

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Tujuan analisis sentimen aplikasi dan produk adalah untuk menentukan polaritas sentimen dalam ulasan aplikasi berdasarkan umpan balik pengguna. Model LSTM dan turunannya, seperti GRU, menjadi semakin populer di antara berbagai desain Jaringan Neural yang digunakan dalam analisis sentimen. Namun, model LSTM masih memiliki masalah terkait lambatnya konvergensi dan hanya dapat mengumpulkan data dalam satu arah, sehingga untuk meningkatkan kinerja, teknik pembelajaran mendalam lainnya seperti Bi-LSTM harus digunakan. Selain itu, kombinasi penyematan kata dapat dipertimbangkan untuk menghasilkan variasi representasi kata yang semakin besar. Penelitian ini menyajikan perbandingan model LSTM dan BiLSTM untuk mengetahui apakah kinerja kombinasi model BiLSTM dengan penerapan word embedding mampu memiliki kinerja yang lebih baik dibandingkan model LSTM satu arah. Hasil pengujian akurasi menunjukkan model BiLSTM dengan Word2Vec mendapatkan hasil pengujian akurasi tertinggi, dengan akurasi sebesar 87%. Hal ini membuktikan bahwa model BiLSTM dengan Word2Vec mampu memiliki performa yang lebih baik dibandingkan LSTM.

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Section: Word2vecmentioning

confidence: 99%

Combining Bi-LSTM And Word2vec Embedding For Sentiment Analysis Models Of Application User Reviews

Verra Budhi Lestari,

Ema Utami,

Hanafi

2024

ijcs

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“…Additionally, the APIs must address functional consistency to maintain reliability across varied deployments [18]. Ethical considerations.…”

Section: Analysis Of Openai's Apimentioning

confidence: 99%

Effective documentation practices for enhancing user interaction through GPT-powered conversational interfaces

Sheremet,

Sadovoi,

Sheremet

et al. 2024

AAIT

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The article presents a detailed overview of the integration of ChatGPT with PDF documents using the LangChain infrastruc-ture, highlighting significant advances in natural language processing and information retrieval. This approach offers the advantage of not being limited to working exclusively with PDF documents. By leveraging the special capabilities of the LangChain infrastruc-ture, it is possible to interact with any data files containing text information. The literature review highlights the transformative im-pact of OpenAI's GPT series of models on natural language processing, with advancements in GPT-4 significantly enhancing the generation of human-like text and setting new standards for interactive artificial intelligenceapplications. The analysis of OpenAI's application programming interface demonstrates its significant role in advancing the integration of artificial intelligence into various applications by providing accessible and robust tools that enable developers and enterprises to seamlessly incorporate sophisticated artificial intelligence functionalities. Despite their advantages, these interfaces face challenges such as latency, processing capacity limitations, and ethical considerations, which necessitate strategic implementation and continuous evaluation to fully harness their potential. The article examines the role of vector data representations, particularly vector embeddings, in enhancing the functionality of artificial intelligence and machine learning systems. These embeddings transform complex textual data into high-dimensional numerical formats, enabling artificial intelligence models to perform tasks such as language understanding, text generation, and data analysis with increased precision and depth. Vector databases play a critical role in managing and leveraging high-dimensional data, specifically vector embeddings, to enhance the operational efficiency of large language models. These specialized storage systems are optimized for handling complex data representations, enabling advanced applications such as text summarization, translation, and question-answering with high accuracy and contextual understanding. LangChain provides a versatile framework that bridges large language models and diverse data sources by utilizing vector databases. This integration enhances the AI's capabilities in data analy-sis and natural language processing, enabling sophisticated applications that can efficiently interpret and respond to user queries across various datasets. Developing a comprehensive application using LangChain and ChatGPT for PDF document interaction re-quires meticulous technical considerations. Key elements include efficient data management through LangChain's data loaders and text splitters, which transform PDFs into manageable formatsand ensure coherent segmentation for accurate AI interaction. Addi-tionally, implementing vector embeddings enhances the AI's ability to comprehend and analyze textual data, while a user-friendly interface and robust security measures ensure optimal user engagement and data protection. The practical implications of this tech-nology are significant, with potential improvements in customer support by reducing resolution times by up to 40%, streamlining academic literature reviews by approximately 60%, and boosting productivity in data analysis by saving an estimated 50% of the time spent on manual data extraction.

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“…For instance, the Topic2vec and Doc2vec models were proposed by combining Word2vec′s locality of context word relationships with LDA’s global representation of topic modeling, to improve the application efficiency of LDA topic models. 47 This study focuses on analyzing short texts from users within an online fitness community. To achieve this, we utilize Word2vec, a word vector training tool developed by Google, along with an LDA theme model to construct our thematic framework.…”

Section: Related Workmentioning

confidence: 99%

Fitness or socializing - A multi-dimensional analysis of online fitness communities users

Wang,

Feng,

Zhang

et al. 2024

iScience

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Comparison of Word2vec and Doc2vec Methods for Text Classification of Product Reviews

Cited by 5 publications

References 21 publications

Combining Bi-LSTM And Word2vec Embedding For Sentiment Analysis Models Of Application User Reviews

Combining Bi-LSTM And Word2vec Embedding For Sentiment Analysis Models Of Application User Reviews

Effective documentation practices for enhancing user interaction through GPT-powered conversational interfaces

Fitness or socializing - A multi-dimensional analysis of online fitness communities users

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