%0 Journal Article %1 noauthororeditor %A Wei, DU Jing %D 2019 %J International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI) %K Deep LDA Language Natural Sentiment Two-way and classification. cycle learning long-term memory model network processing short-term topic %N 4 %P 7 %R 10.5121/ijscai.2019.8402 %T The Effects of the LDA Topic Model on Sentiment Classification %U http://aircconline.com/ijscai/V8N4/8419ijscai02.pdf %V 8 %X Online reviews are a feedback to the product and play a key role in improving the product to cater to consumers. Online reviews that rely heavily on manual categorization are time consuming and labor intensive.The recurrent neural network in deep learning can process time series data, while the long and short term memory network can process long time sequence data well. This has good experimental verification support in natural language processing, machine translation, speech recognition and language model.The merits of the extracted data features affect the classification results produced by the classification model. The LDA topic model adds a priori a posteriori knowledge to classify the data so that the characteristics of the data can be extracted efficiently.Applied to the classifier can improve accuracy and efficiency. Two-way long-term and short-term memory networks are variants and extensions of cyclic neural networks.The deep learning framework Keras uses Tensorflow as the backend to build a convenient two-way long-term and short-term memory network model, which provides a strong technical support for the experiment.Using the LDA topic model to extract the keywords needed to train the neural network and increase the internal relationship between words can improve the learning efficiency of the model. The experimental results in the same experimental environment are better than the traditional word frequency features.

@article{noauthororeditor, abstract = {Online reviews are a feedback to the product and play a key role in improving the product to cater to consumers. Online reviews that rely heavily on manual categorization are time consuming and labor intensive.The recurrent neural network in deep learning can process time series data, while the long and short term memory network can process long time sequence data well. This has good experimental verification support in natural language processing, machine translation, speech recognition and language model.The merits of the extracted data features affect the classification results produced by the classification model. The LDA topic model adds a priori a posteriori knowledge to classify the data so that the characteristics of the data can be extracted efficiently.Applied to the classifier can improve accuracy and efficiency. Two-way long-term and short-term memory networks are variants and extensions of cyclic neural networks.The deep learning framework Keras uses Tensorflow as the backend to build a convenient two-way long-term and short-term memory network model, which provides a strong technical support for the experiment.Using the LDA topic model to extract the keywords needed to train the neural network and increase the internal relationship between words can improve the learning efficiency of the model. The experimental results in the same experimental environment are better than the traditional word frequency features. }, added-at = {2022-04-08T07:23:48.000+0200}, author = {Wei, DU Jing}, biburl = {https://www.bibsonomy.org/bibtex/2bb8846a5c0fe513f63bc583ec4367b62/leninsha}, doi = {10.5121/ijscai.2019.8402}, interhash = {7863e9dd086d3d23aa795424c933b6e8}, intrahash = {bb8846a5c0fe513f63bc583ec4367b62}, issn = {2319 - 1015}, journal = {International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI)}, keywords = {Deep LDA Language Natural Sentiment Two-way and classification. cycle learning long-term memory model network processing short-term topic}, language = {English}, month = {November}, number = 4, pages = 7, timestamp = {2022-04-08T07:23:48.000+0200}, title = {The Effects of the LDA Topic Model on Sentiment Classification}, url = {http://aircconline.com/ijscai/V8N4/8419ijscai02.pdf}, volume = 8, year = 2019 }