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Automated Classification of Text Sentiment

The ability to identify sentiment in text, referred to as sentiment analysis, is one which is natural to adult humans. This task is, how- ever, not one which a computer can perform by default. Identifying sentiments in an automated, algorithmic manner will be a useful capability for business and research in their search to understand what consumers think about their products or services and to un- derstand human sociology. Here we propose two new Genetic Al- gorithms (GAs) for the task of automated text sentiment analysis. The G As learn whether words occurring in a text corpus are ei- ther sentiment or amplifier words, and their corresponding magni- tude. Sentiment words, such as ’horrible’, add linearly to the final sentiment. Amplifier words in contrast, which are typically adjec- tives/adverbs like ’very’, multiply the sentiment of the following word. This increases, decreases or negates the sentiment of the fol- lowing word. The sentiment of the full text is then the sum of these terms. This approach grows both a sentiment and amplifier dictio- nary which can be reused for other purposes and fed into other machine learning algorithms. We report the results of multiple ex- periments conducted on large Amazon data sets. The results reveal that our proposed approach was able to outperform several public and/or commercial sentiment analysis algorithms. ...展开详情收缩
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Cross-Domain Sentiment Classification via Spectral Feature Alignment

Cross-Domain Sentiment Classification via Spectral Feature Alignment

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Neural Sentiment Classification with User and Product Attention

Neural Sentiment Classification with User and Product Attention

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Recurrent Convolutional Neural Networks for Text Classification

找到了国外的一篇文章《Recurrent Convolutional Neural Network For Text Classification》

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Text Classification example

Text Classification example 文本分类 例子 Text Classification example 文本分类 例子 Text Classification example 文本分类 例子 Text Classification example 文本分类 例子

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Text Analytics with Python

■■Chapter 1: Natural Language Basics .............................................. 1 ■■Chapter 2: Python Refresher ........................................................ 51 ■■Chapter 3: Processing and Understanding Text .......................... 107 ■■Chapter 4: Text Classification ..................................................... 167 ■■Chapter 5: Text Summarization .................................................. 217 ■■Chapter 6: Text Similarity and Clustering ................................... 265 ■■Chapter 7: Semantic and Sentiment Analysis ............................ 319

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A C-LSTM Neural Network for Text Classification.pdf

一篇论文,结合了cnn和lstm的深度网络用来做文本分类。

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Sentiment Analysis Mining Opinions Sentiments and Emotions

Hardcover: 381 pages Publisher: Cambridge University Press; 1 edition (June 4, 2015) Language: English Book Description This comprehensive introduction to all the core areas and many emerging themes of sentiment analysis approaches the problem from a natural-language-processing angle. The author explains the underlying structure and the language constructs that are commonly used to express opinions and sentiments and presents computational methods to analyze and summarize opinions.

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Transductive_inference_for_text_classification_using_support_vector_machines

英文版,关于支持向量机(svm)的比较权威的论文,对算法有较详尽的描述,想了解这方面知识的建议一读

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Current State of Text Sentiment Analysis from Opinion

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text mining text classification(文本挖掘,文本分类)最全、最经典电子书合集(原版电子书,非扫描版)

1,The Text Mining Handbook.Advanced Approaches in Analyzing Unstructured Data.pdf 2,Foundations_of_Statistical_Natural_Language_Processing.pdf 3,Survey_of_Text_Mining_II_Clustering_Classification_and_Retrieval.pdf 4,Text+Mining-Classification,+Clustering,+and+Applications.pdf

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Attention-based LSTM for Aspect-level Sentiment Classification 论文代码

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NLPCC2014 微博情感分析样例数据

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Machine Learning in Automated Text Categorization.pdf

Machine Learning in Automated Text Categorization.pdf

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Text.Analytics.with.Python

Derive useful insights from your data using Python. Learn the techniques related to natural language processing and text analytics, and gain the skills to know which technique is best suited to solve a particular problem. Text Analytics with Python teaches you both basic and advanced concepts, including text and language syntax, structure, semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization. A structured and comprehensive approach is followed in this book so that readers with little or no experience do not find themselves overwhelmed. You will start with the basics of natural language and Python and move on to advanced analytical and machine learning concepts. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems. This book Provides complete coverage of the major concepts and techniques of natural language processing (NLP) and text analytics Includes practical real-world examples of techniques for implementation, such as building a text classification system to categorize news articles, analyzing app or game reviews using topic modeling and text summarization, and clustering popular movie synopses and analyzing the sentiment of movie reviews Shows implementations based on Python and several popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern What you will learn Natural Language concepts Analyzing Text syntax and structure Text Classification Text Clustering and Similarity analysis Text Summarization Semantic and Sentiment analysis Readership The book is for IT professionals, analysts, developers, linguistic experts, data scientists, and anyone with a keen interest in linguistics, analytics, and generating insights from textual data. Table of Contents Chapter 1: Natural Language Basics Chapter 2: Python Refresher Chapter 3: Processing and Understanding Text Chapter 4: Text Classification Chapter 5: Text Summarization Chapter 6: Text Similarity and Clustering Chapter 7: Semantic and Sentiment Analysis

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Machine Learning in Automated Text Categorization

文本分类方面比较经典的文章

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stanfordSentimentTreebank.zip

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classification文本分类工具包

该工具包包含了完整的文本分类流程,涉及分词、特征处理、模型训练、未知样本分类等,是初级文本分类必备工具包

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Automated Trading with R(Apress,2016)

This book explains the broad topic of automated trading, starting with its mathematics and moving to its computation and execution. Readers will gain a unique insight into the mechanics and computational considerations taken in building a backtester, strategy optimizer, and fully functional trading platform. Automated Trading with R provides automated traders with all the tools they need to trade algorithmically with their existing brokerage, from data management, to strategy optimization, to order execution, using free and publically available data. If your brokerage’s API is supported, the source code is plug-and-play. The platform built in this book can serve as a complete replacement for commercially available platforms used by retail traders and small funds. Software components are strictly decoupled and easily scalable, providing opportunity to substitute any data source, trading algorithm, or brokerage. The book’s three objectives are: To provide a flexible alternative to common strategy automation frameworks, like Tradestation, Metatrader, and CQG, to small funds and retail traders. To offer an understanding the internal mechanisms of an automated trading system. To standardize discussion and notation of real-world strategy optimization problems. What you’ll learn Programming an automated strategy in R gives the trader access to R and its package library for optimizing strategies, generating real-time trading decisions, and minimizing computation time. How to best simulate strategy performance in their specific use case to derive accurate performance estimates. Important machine-learning criteria for statistical validity in the context of time-series. An understanding of critical real-world variables pertaining to portfolio management and performance assessment, including latency, drawdowns, varying trade size, portfolio growth, and penalization of unused capital.

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