A deep learning architecture for sentiment analysis

Çano, Erion; Morisio, Maurizio

doi:10.1145/3220228.3220229

Cited by 12 publications

(7 citation statements)

References 15 publications

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“…We involving word embedding, in this case, we applied GloVe [30] and CNN that belonging responsible for generating sentence latent factor vector. As we know that CNN output fit for classification issue [28], [31] and also this approach are not suitable to regression application, So required to incorporating with any method that common use in rating prediction. In our opinion, the matrix factorization method is the right choice to generate rating prediction.…”

Section: Methodsmentioning

confidence: 99%

Convolutional-NN and Word Embedding for Making an Effective Product Recommendation Based on Enhanced Contextual Understanding of a Product Review

Hanafi¹,

Suryana

Basari

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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E-commerce is one of the most popular service applications in the world in the last decade. It has become a revolutionary model from traditional shopping transaction to entire internet commerce. E-commerce needs essential artificial intelligence (AI) to provide the customer with information about a product, called a recommendation machine. Collaborative filtering is a model of a recommendation algorithm that relies on rating as the fundamental calculation to make a recommendation. It has been successfully implemented in e-commerce. Even so, this model has a weakness in sparse product data in which the rating number is very low or sparse. Mostly, only less than 3% of the total user population rate a product, leading to the rise of sparse data. A text sentence document is a part of customers' feedback that can be converted into a product rating. According to a traditional approach, bag of word and lexicon model are ignored in a contextual approach. This experiment, it developed a new model to increase the contextuality of text sentences, leading to a more effective rating prediction. We employed a kind of convolutional neural network to generate item latent factor vectors that could be incorporated with probabilistic matrix factorization to make rating prediction. Our method outperformed several previous works based on a metric evaluation using the Root Mean Squared Error (RMSE). In this experiment, we analyzed MovieLens and IMDB datasets, which contained a movie product review.

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Section: Methodsmentioning

confidence: 99%

Convolutional-NN and Word Embedding for Making an Effective Product Recommendation Based on Enhanced Contextual Understanding of a Product Review

Hanafi¹,

Suryana

Basari

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…In 2018, Cano and Morisio [9] also conducted sentiment analysis using Film Review Data from the IMDB website. The architectural model used is NGramCNN, compared to the SingleCNN and BLSTM-2DCNN models.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Transjakarta Based on Twitter using Convolutional Neural Network

Yudi¹,

Suharjito²

2019

Adv. sci. technol. eng. syst. j.

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TransJakarta is one of the methods to reduce congestion in Jakarta. However, the number of TransJakarta users compared to number of private vehicle users is very small, only 24% of the total population in Jakarta. The purpose of this research is to know public opinions about TransJakarta whether positive or negative by doing sentiment analysis about TransJakarta based on the opinion of Twitter, as Twitter is one of media to express its many users to express their opinions about an individual or an instance. Data is retrieved from Twitter using the R-Studio application by utilizing the "TwitteR" library, then preprocessing and stored in a database. Next step is labelling the data using Sengon Lexicon and will be trained and tested using the Convolutional Neural Network algorithm. There are three CNN architectural models to be tested, namely VGG, ResNet, and GoogleNet. The designed VGG consists of 16 layers, ResNet 34 layers, and GoogleNet 22 layers. After the data are trained and tested, the results will be evaluated using Confusion Matrix to get the best F-Score. The results showed that among the three architectural models that were tested, the Resnet 34 layers architecture model gave the best F-Score of 98.11%, better compared to VGG which had the highest F-Score value of 96.74% and GoogleNet of 96.80%.

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“…It still takes a lot of text data (e.g., many thousands of samples) to generate high-quality embeddings and achieve reasonable classification performance (Ç ano and Morisio, 2017). A neural network architecture for sentiment analysis based on word embeddings is described by (Ç ano and Morisio, 2018). We applied that architecture on the two Czech datasets we are using here and observed that there was severe over-fitting, even with dropout regularization.…”

Section: Preprocessing and Vectorizationmentioning

confidence: 99%

Sentiment Analysis of Czech Texts: An Algorithmic Survey

Çano

Bojar

2019

Proceedings of the 11th International Conference on Agents and Artificial Intelligence

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In the area of online communication, commerce and transactions, analyzing sentiment polarity of texts written in various natural languages has become crucial. While there have been a lot of contributions in resources and studies for the English language, "smaller" languages like Czech have not received much attention. In this survey, we explore the effectiveness of many existing machine learning algorithms for sentiment analysis of Czech Facebook posts and product reviews. We report the sets of optimal parameter values for each algorithm and the scores in both datasets. We finally observe that support vector machines are the best classifier and efforts to increase performance even more with bagging, boosting or voting ensemble schemes fail to do so.

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A deep learning architecture for sentiment analysis

Cited by 12 publications

References 15 publications

Convolutional-NN and Word Embedding for Making an Effective Product Recommendation Based on Enhanced Contextual Understanding of a Product Review

Convolutional-NN and Word Embedding for Making an Effective Product Recommendation Based on Enhanced Contextual Understanding of a Product Review

Sentiment Analysis of Transjakarta Based on Twitter using Convolutional Neural Network

Sentiment Analysis of Czech Texts: An Algorithmic Survey

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