New Product Development (NPD) through Social Media-based Analysis by Comparing Word2Vec and BERT Word Embeddings

• URL: http://arxiv.org/abs/2304.08369v1
• Date: Mon, 17 Apr 2023 15:32:11 GMT
• Title: New Product Development (NPD) through Social Media-based Analysis by Comparing Word2Vec and BERT Word Embeddings
• Authors: Princessa Cintaqia and Matheus Inoue
• Abstract summary: Two popular word embedding techniques, Word2Vec and BERT, were evaluated to identify the best-performing approach in sentiment analysis and opinion detection. BERT word embeddings combined with Balanced Random Forest yielded the most accurate single model for both sentiment analysis and opinion detection.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study introduces novel methods for sentiment and opinion classification of tweets to support the New Product Development (NPD) process. Two popular word embedding techniques, Word2Vec and BERT, were evaluated as inputs for classic Machine Learning and Deep Learning algorithms to identify the best-performing approach in sentiment analysis and opinion detection with limited data. The results revealed that BERT word embeddings combined with Balanced Random Forest yielded the most accurate single model for both sentiment analysis and opinion detection on a use case. Additionally, the paper provides feedback for future product development performing word graph analysis of the tweets with same sentiment to highlight potential areas of improvement.

Related papers

• Beyond Coarse-Grained Matching in Video-Text Retrieval [50.799697216533914]
  We introduce a new approach for fine-grained evaluation. Our approach can be applied to existing datasets by automatically generating hard negative test captions. Experiments on our fine-grained evaluations demonstrate that this approach enhances a model's ability to understand fine-grained differences.
  arXiv  Detail & Related papers  (2024-10-16T09:42:29Z)
• Explaining Hate Speech Classification with Model Agnostic Methods [0.9990687944474738]
  The research goal of this paper is to bridge the gap between hate speech prediction and the explanations generated by the system to support its decision. This has been achieved by first predicting the classification of a text and then providing a posthoc, model agnostic and surrogate interpretability approach.
  arXiv  Detail & Related papers  (2023-05-30T19:52:56Z)
• Pre-trained Embeddings for Entity Resolution: An Experimental Analysis [Experiment, Analysis & Benchmark] [65.11858854040544]
  We perform a thorough experimental analysis of 12 popular language models over 17 established benchmark datasets. First, we assess their vectorization overhead for converting all input entities into dense embeddings vectors. Second, we investigate their blocking performance, performing a detailed scalability analysis, and comparing them with the state-of-the-art deep learning-based blocking method. Third, we conclude with their relative performance for both supervised and unsupervised matching.
  arXiv  Detail & Related papers  (2023-04-24T08:53:54Z)
• BERT-Based Combination of Convolutional and Recurrent Neural Network for Indonesian Sentiment Analysis [0.0]
  This research extends the previous hybrid deep learning using BERT representation for Indonesian sentiment analysis. Our simulation shows that the BERT representation improves the accuracies of all hybrid architectures.
  arXiv  Detail & Related papers  (2022-11-10T00:32:40Z)
• Just Rank: Rethinking Evaluation with Word and Sentence Similarities [105.5541653811528]
  intrinsic evaluation for embeddings lags far behind, and there has been no significant update since the past decade. This paper first points out the problems using semantic similarity as the gold standard for word and sentence embedding evaluations. We propose a new intrinsic evaluation method called EvalRank, which shows a much stronger correlation with downstream tasks.
  arXiv  Detail & Related papers  (2022-03-05T08:40:05Z)
• A Comparative Study of Transformers on Word Sense Disambiguation [0.0]
  This paper presents a comparative study on the contextualization power of neural network-based embedding systems. We evaluate their contextualization power using two sample Word Sense Disambiguation (WSD) tasks, SensEval-2 and SensEval-3. Experimental results show that the proposed techniques also achieve superior results over the current state-of-the-art on both the WSD tasks.
  arXiv  Detail & Related papers  (2021-11-30T14:10:22Z)
• Sentiment analysis in tweets: an assessment study from classical to modern text representation models [59.107260266206445]
  Short texts published on Twitter have earned significant attention as a rich source of information. Their inherent characteristics, such as the informal, and noisy linguistic style, remain challenging to many natural language processing (NLP) tasks. This study fulfils an assessment of existing language models in distinguishing the sentiment expressed in tweets by using a rich collection of 22 datasets.
  arXiv  Detail & Related papers  (2021-05-29T21:05:28Z)
• Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning [89.64620296557177]
  We propose to incorporate the syntactic structures of the sentences into the deep learning models for targeted opinion word extraction. We also introduce a novel regularization technique to improve the performance of the deep learning models. The proposed model is extensively analyzed and achieves the state-of-the-art performance on four benchmark datasets.
  arXiv  Detail & Related papers  (2020-10-26T07:13:17Z)
• Improving BERT Performance for Aspect-Based Sentiment Analysis [3.5493798890908104]
  Aspect-Based Sentiment Analysis (ABSA) studies the consumer opinion on the market products. It involves examining the type of sentiments as well as sentiment targets expressed in product reviews. We show that applying the proposed models eliminates the need for further training of the BERT model.
  arXiv  Detail & Related papers  (2020-10-22T13:52:18Z)
• Latent Opinions Transfer Network for Target-Oriented Opinion Words Extraction [63.70885228396077]
  We propose a novel model to transfer opinions knowledge from resource-rich review sentiment classification datasets to low-resource task TOWE. Our model achieves better performance compared to other state-of-the-art methods and significantly outperforms the base model without transferring opinions knowledge.
  arXiv  Detail & Related papers  (2020-01-07T11:50:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.