Sentiment Analysis Using Averaged Weighted Word Vector Features
- URL: http://arxiv.org/abs/2002.05606v2
- Date: Sun, 15 Oct 2023 20:36:10 GMT
- Title: Sentiment Analysis Using Averaged Weighted Word Vector Features
- Authors: Ali Erkan and Tunga Gungor
- Abstract summary: We develop two methods that combine different types of word vectors to learn and estimate polarity of reviews.
We apply the methods to several datasets from different domains that are used as standard benchmarks for sentiment analysis.
- Score: 1.2691047660244332
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: People use the world wide web heavily to share their experience with entities
such as products, services, or travel destinations. Texts that provide online
feedback in the form of reviews and comments are essential to make consumer
decisions. These comments create a valuable source that may be used to measure
satisfaction related to products or services. Sentiment analysis is the task of
identifying opinions expressed in such text fragments. In this work, we develop
two methods that combine different types of word vectors to learn and estimate
polarity of reviews. We develop average review vectors from word vectors and
add weights to this review vectors using word frequencies in positive and
negative sensitivity-tagged reviews. We applied the methods to several datasets
from different domains that are used as standard benchmarks for sentiment
analysis. We ensemble the techniques with each other and existing methods, and
we make a comparison with the approaches in the literature. The results show
that the performances of our approaches outperform the state-of-the-art success
rates.
Related papers
- A New Generation of Perspective API: Efficient Multilingual
Character-level Transformers [66.9176610388952]
We present the fundamentals behind the next version of the Perspective API from Google Jigsaw.
At the heart of the approach is a single multilingual token-free Charformer model.
We demonstrate that by forgoing static vocabularies, we gain flexibility across a variety of settings.
arXiv Detail & Related papers (2022-02-22T20:55:31Z) - SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item
Recommendation [48.1799451277808]
We propose a Sentiment-aware Interactive Fusion Network (SIFN) for review-based item recommendation.
We first encode user/item reviews via BERT and propose a light-weighted sentiment learner to extract semantic features of each review.
Then, we propose a sentiment prediction task that guides the sentiment learner to extract sentiment-aware features via explicit sentiment labels.
arXiv Detail & Related papers (2021-08-18T08:04:38Z) - SentiLSTM: A Deep Learning Approach for Sentiment Analysis of Restaurant
Reviews [13.018530502810128]
This paper proposes, a deep learning-based technique (i.e., BiLSTM) to classify the reviews provided by the clients of the restaurant into positive and negative polarities.
The results of the evaluation on test dataset show that BiLSTM technique produced in the highest accuracy of 91.35%.
arXiv Detail & Related papers (2020-11-19T06:24:42Z) - Hierarchical Bi-Directional Self-Attention Networks for Paper Review
Rating Recommendation [81.55533657694016]
We propose a Hierarchical bi-directional self-attention Network framework (HabNet) for paper review rating prediction and recommendation.
Specifically, we leverage the hierarchical structure of the paper reviews with three levels of encoders: sentence encoder (level one), intra-review encoder (level two) and inter-review encoder (level three)
We are able to identify useful predictors to make the final acceptance decision, as well as to help discover the inconsistency between numerical review ratings and text sentiment conveyed by reviewers.
arXiv Detail & Related papers (2020-11-02T08:07:50Z) - Weakly-Supervised Aspect-Based Sentiment Analysis via Joint
Aspect-Sentiment Topic Embedding [71.2260967797055]
We propose a weakly-supervised approach for aspect-based sentiment analysis.
We learn sentiment, aspect> joint topic embeddings in the word embedding space.
We then use neural models to generalize the word-level discriminative information.
arXiv Detail & Related papers (2020-10-13T21:33:24Z) - A Unified Dual-view Model for Review Summarization and Sentiment
Classification with Inconsistency Loss [51.448615489097236]
Acquiring accurate summarization and sentiment from user reviews is an essential component of modern e-commerce platforms.
We propose a novel dual-view model that jointly improves the performance of these two tasks.
Experiment results on four real-world datasets from different domains demonstrate the effectiveness of our model.
arXiv Detail & Related papers (2020-06-02T13:34:11Z) - Topic Detection and Summarization of User Reviews [6.779855791259679]
We propose an effective new summarization method by analyzing both reviews and summaries.
A new dataset comprising product reviews and summaries about 1028 products are collected from Amazon and CNET.
arXiv Detail & Related papers (2020-05-30T02:19:08Z) - A computational model implementing subjectivity with the 'Room Theory'.
The case of detecting Emotion from Text [68.8204255655161]
This work introduces a new method to consider subjectivity and general context dependency in text analysis.
By using similarity measure between words, we are able to extract the relative relevance of the elements in the benchmark.
This method could be applied to all the cases where evaluating subjectivity is relevant to understand the relative value or meaning of a text.
arXiv Detail & Related papers (2020-05-12T21:26:04Z) - Generating Word and Document Embeddings for Sentiment Analysis [0.36525095710982913]
In this paper, we combine contextual and supervised information with the general semantic representations of words occurring in the dictionary.
We induce domain-specific sentimental vectors for two corpora, which are the movie domain and the Twitter datasets in Turkish.
It shows that our approaches are cross-domain and portable to other languages.
arXiv Detail & Related papers (2020-01-05T16:34:32Z)
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.