Semisupervised sentiment analysis method for online text reviews

2020 ◽  
pp. 016555152091003
Author(s):  
Gyeong Taek Lee ◽  
Chang Ouk Kim ◽  
Min Song
Keyword(s):  
Unsupervised Learning ◽  
Sentiment Analysis ◽  
Supervised Learning ◽  
Model Space ◽  
Training Dataset ◽  
Learning Approach ◽  
Learning Models ◽  
Text Data ◽  
Learning Techniques ◽  
Sentiment Dictionary

Sentiment analysis plays an important role in understanding individual opinions expressed in websites such as social media and product review sites. The common approaches to sentiment analysis use the sentiments carried by words that express opinions and are based on either supervised or unsupervised learning techniques. The unsupervised learning approach builds a word-sentiment dictionary, but it requires lengthy time periods and high costs to build a reliable dictionary. The supervised learning approach uses machine learning models to learn the sentiment scores of words; however, training a classifier model requires large amounts of labelled text data to achieve a good performance. In this article, we propose a semisupervised approach that performs well despite having only small amounts of labelled data available for training. The proposed method builds a base sentiment dictionary from a small training dataset using a lasso-based ensemble model with minimal human effort. The scores of words not in the training dataset are estimated using an adaptive instance-based learning model. In a pretrained word2vec model space, the sentiment values of the words in the dictionary are propagated to the words that did not exist in the training dataset. Through two experiments, we demonstrate that the performance of the proposed method is comparable to that of supervised learning models trained on large datasets.

Classification Based on Unsupervised Learning

2011 ◽  
pp. 348-395
Author(s):  
Yu Wang
Keyword(s):  
Network Security ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Network Traffic ◽  
High Speed ◽  
Ad Hoc ◽  
Training Data ◽  
Traffic Data ◽  
Response Variable ◽  
Learning Techniques

The requirement for having a labeled response variable in training data from the supervised learning technique may not be satisfied in some situations: particularly, in dynamic, short-term, and ad-hoc wireless network access environments. Being able to conduct classification without a labeled response variable is an essential challenge to modern network security and intrusion detection. In this chapter we will discuss some unsupervised learning techniques including probability, similarity, and multidimensional models that can be applied in network security. These methods also provide a different angle to analyze network traffic data. For comprehensive knowledge on unsupervised learning techniques please refer to the machine learning references listed in the previous chapter; for their applications in network security see Carmines, Edward & McIver (1981), Lane & Brodley (1997), Herrero, Corchado, Gastaldo, Leoncini, Picasso & Zunino (2007), and Dhanalakshmi & Babu (2008). Unlike in supervised learning, where for each vector 1 2 ( , , , ) n X x x x = ? we have a corresponding observed response, Y, in unsupervised learning we only have X, and Y is not available either because we could not observe it or its frequency is too low to be fit ted with a supervised learning approach. Unsupervised learning has great meanings in practice because in many circumstances, available network traffic data may not include any anomalous events or known anomalous events (e.g., traffics collected from a newly constructed network system). While high-speed mobile wireless and ad-hoc network systems have become popular, the importance and need to develop new unsupervised learning methods that allow the modeling of network traffic data to use anomaly-free training data have significantly increased.

Emergent Languages from Pretrained Embeddings Characterize Latent Concepts in Dynamic Imagery

2020 ◽  
Vol 14 (03) ◽  
pp. 357-373
Author(s):  
James R. Kubricht ◽  
Alberto Santamaria-Pang ◽  
Chinmaya Devaraj ◽  
Aritra Chowdhury ◽  
Peter Tu
Keyword(s):  
Unsupervised Learning ◽  
Supervised Learning ◽  
Semantic Analysis ◽  
Single Agent ◽  
Learning Approach ◽  
Learning Approaches ◽  
Physical Motion ◽  
Semantic Concepts ◽  
Dynamic Imagery ◽  
Motion Types

Recent unsupervised learning approaches have explored the feasibility of semantic analysis and interpretation of imagery using Emergent Language (EL) models. As EL requires some form of numerical embedding as input, it remains unclear which type is required in order for the EL to properly capture key semantic concepts associated with a given domain. In this paper, we compare unsupervised and supervised approaches for generating embeddings across two experiments. In Experiment 1, data are produced using a single-agent simulator. In each episode, a goal-driven agent attempts to accomplish a number of tasks in a synthetic cityscape environment which includes houses, banks, theaters and restaurants. In Experiment 2, a comparatively smaller dataset is produced where one or more objects demonstrate various types of physical motion in a 3D simulator environment. We investigate whether EL models generated from embeddings of raw pixel data produce expressions that capture key latent concepts (i.e. an agent’s motivations or physical motion types) in each environment. Our initial experiments show that the supervised learning approaches yield embeddings and EL descriptions that capture meaningful concepts from raw pixel inputs. Alternatively, embeddings from an unsupervised learning approach result in greater ambiguity with respect to latent concepts.

scholarly journals A Semi-Supervised Learning Approach for Tackling Twitter Spam Drift

2019 ◽  
Vol 18 (02) ◽  
pp. 1950010 ◽  
Author(s):  
Niddal Imam ◽  
Biju Issac ◽  
Seibu Mary Jacob
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Research Community ◽  
Machine Learning Techniques ◽  
Spam Detection ◽  
Learning Approach ◽  
New Approach ◽  
Detection Systems ◽  
Learning Techniques ◽  
Over Time

Twitter has changed the way people get information by allowing them to express their opinion and comments on the daily tweets. Unfortunately, due to the high popularity of Twitter, it has become very attractive to spammers. Unlike other types of spam, Twitter spam has become a serious issue in the last few years. The large number of users and the high amount of information being shared on Twitter play an important role in accelerating the spread of spam. In order to protect the users, Twitter and the research community have been developing different spam detection systems by applying different machine-learning techniques. However, a recent study showed that the current machine learning-based detection systems are not able to detect spam accurately because spam tweet characteristics vary over time. This issue is called “Twitter Spam Drift”. In this paper, a semi-supervised learning approach (SSLA) has been proposed to tackle this. The new approach uses the unlabeled data to learn the structure of the domain. Different experiments were performed on English and Arabic datasets to test and evaluate the proposed approach and the results show that the proposed SSLA can reduce the effect of Twitter spam drift and outperform the existing techniques.

scholarly journals ML-CB: Machine Learning Canvas Block

2021 ◽  
Vol 2021 (3) ◽  
pp. 453-473
Author(s):  
Nathan Reitinger ◽  
Michelle L. Mazurek
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Semantic Representation ◽  
Source Code ◽  
Online Privacy ◽  
Learning Approach ◽  
Learning Models ◽  
One Step ◽  
The Web ◽  
Machine Learning Models

Abstract With the aim of increasing online privacy, we present a novel, machine-learning based approach to blocking one of the three main ways website visitors are tracked online—canvas fingerprinting. Because the act of canvas fingerprinting uses, at its core, a JavaScript program, and because many of these programs are reused across the web, we are able to fit several machine learning models around a semantic representation of a potentially offending program, achieving accurate and robust classifiers. Our supervised learning approach is trained on a dataset we created by scraping roughly half a million websites using a custom Google Chrome extension storing information related to the canvas. Classification leverages our key insight that the images drawn by canvas fingerprinting programs have a facially distinct appearance, allowing us to manually classify files based on the images drawn; we take this approach one step further and train our classifiers not on the malleable images themselves, but on the more-difficult-to-change, underlying source code generating the images. As a result, ML-CB allows for more accurate tracker blocking.

scholarly journals Quantifying Seagrass Distribution in Coastal Water with Deep Learning Models

2020 ◽  
Vol 12 (10) ◽  
pp. 1581 ◽  
Author(s):  
Daniel Perez ◽  
Kazi Islam ◽  
Victoria Hill ◽  
Richard Zimmerman ◽  
Blake Schaeffer ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Transfer Learning ◽  
Satellite Images ◽  
Support Vector ◽  
Learning Approach ◽  
Learning Models ◽  
Learning Techniques ◽  
The World ◽  
New Locations

Coastal ecosystems are critically affected by seagrass, both economically and ecologically. However, reliable seagrass distribution information is lacking in nearly all parts of the world because of the excessive costs associated with its assessment. In this paper, we develop two deep learning models for automatic seagrass distribution quantification based on 8-band satellite imagery. Specifically, we implemented a deep capsule network (DCN) and a deep convolutional neural network (CNN) to assess seagrass distribution through regression. The DCN model first determines whether seagrass is presented in the image through classification. Second, if seagrass is presented in the image, it quantifies the seagrass through regression. During training, the regression and classification modules are jointly optimized to achieve end-to-end learning. The CNN model is strictly trained for regression in seagrass and non-seagrass patches. In addition, we propose a transfer learning approach to transfer knowledge in the trained deep models at one location to perform seagrass quantification at a different location. We evaluate the proposed methods in three WorldView-2 satellite images taken from the coastal area in Florida. Experimental results show that the proposed deep DCN and CNN models performed similarly and achieved much better results than a linear regression model and a support vector machine. We also demonstrate that using transfer learning techniques for the quantification of seagrass significantly improved the results as compared to directly applying the deep models to new locations.

scholarly journals Building a New Sentiment Analysis Dataset for Uzbek Language and Creating Baseline Models

Proceedings ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 37
Author(s):  
Elmurod Kuriyozov ◽  
Sanatbek Matlatipov
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Techniques ◽  
Medium Size ◽  
Learning Models ◽  
Important Goal ◽  
Processing Technologies ◽  
Learning Techniques ◽  
Polarity Classification ◽  
Analysis Models

Making natural language processing technologies available for low-resource languages is an important goal to improve the access to technology in their communities of speakers. In this paper, we provide the first annotated corpora for polarity classification for Uzbek language. Our methodology considers collecting a medium-size manually annotated dataset and a larger-size dataset automatically translated from existing resources. Then, we use these datasets to train sentiment analysis models on the Uzbek language, using both traditional machine learning techniques and recent deep learning models.

Application of Machine Learning Algorithms in Stock Market Prediction

2020 ◽  
pp. 153-180
Author(s):  
Sumit Kumar ◽  
Sanlap Acharya
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Better Than

The prediction of stock prices has always been a very challenging problem for investors. Using machine learning techniques to predict stock prices is also one of the favourite topics for academics working in this domain. This chapter discusses five supervised learning techniques and two unsupervised learning techniques to solve the problem of stock price prediction and has compared the performances of all the algorithms. Among the supervised learning techniques, Long Short-Term Memory (LSTM) algorithm performed better than the others whereas, among the unsupervised learning techniques, Restricted Boltzmann Machine (RBM) performed better. RBM is found to be performing even better than LSTM.

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