【导读】专知内容组整理了最近六篇情感分析(Sentiment Analysis)相关文章,为大家进行介绍,欢迎查看!

1. Deep contextualized word representations(深度上下文的词表示)

作者:Matthew E. Peters,Mark Neumann,Mohit Iyyer,Matt Gardner,Christopher Clark,Kenton Lee,Luke Zettlemoyer

机构:University of Washington

摘要:We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals.

期刊:arXiv, 2018年3月23日



2. Sentiment Analysis of Comments on Rohingya Movement with Support Vector Machine(基于支持向量机的对罗兴亚运动评论的情感分析)

作者:Hemayet Ahmed Chowdhury,Tanvir Alam Nibir,Md. Saiful Islam

机构:Shahjalal University of Science and Technology

摘要:The Rohingya Movement and Crisis caused a huge uproar in the political and economic state of Bangladesh. Refugee movement is a recurring event and a large amount of data in the form of opinions remains on social media such as Facebook, with very little analysis done on them.To analyse the comments based on all Rohingya related posts, we had to create and modify a classifier based on the Support Vector Machine algorithm. The code is implemented in python and uses scikit-learn library. A dataset on Rohingya analysis is not currently available so we had to use our own data set of 2500 positive and 2500 negative comments. We specifically used a support vector machine with linear kernel. A previous experiment was performed by us on the same dataset using the naive bayes algorithm, but that did not yield impressive results.

期刊:arXiv, 2018年3月22日



3. ρ-hot Lexicon Embedding-based Two-level LSTM for Sentiment Analysis(基于ρ-hot词典Embedding的两级LSTM情感分析)

作者:Ou Wu,Tao Yang,Mengyang Li,Ming Li

摘要:Sentiment analysis is a key component in various text mining applications. Numerous sentiment classification techniques, including conventional and deep learning-based methods, have been proposed in the literature. In most existing methods, a high-quality training set is assumed to be given. Nevertheless, constructing a high-quality training set that consists of highly accurate labels is challenging in real applications. This difficulty stems from the fact that text samples usually contain complex sentiment representations, and their annotation is subjective. We address this challenge in this study by leveraging a new labeling strategy and utilizing a two-level long short-term memory network to construct a sentiment classifier. Lexical cues are useful for sentiment analysis, and they have been utilized in conventional studies. For example, polar and privative words play important roles in sentiment analysis. A new encoding strategy, that is, $\rho$-hot encoding, is proposed to alleviate the drawbacks of one-hot encoding and thus effectively incorporate useful lexical cues. We compile three Chinese data sets on the basis of our label strategy and proposed methodology. Experiments on the three data sets demonstrate that the proposed method outperforms state-of-the-art algorithms.

期刊:arXiv, 2018年3月21日



4. Multimodal Sentiment Analysis: Addressing Key Issues and Setting up Baselines(多模态情感分析:解决关键问题和建立基准)

作者:Soujanya Poria,Navonil Majumder,Devamanyu Hazarika,Erik Cambria,Amir Hussain,Alexander Gelbukh

摘要:Sentiment analysis is proven to be very useful tool in many applications regarding social media. This has led to a great surge of research in this field. Hence, in this paper, we compile the baselines for such research. In this paper, we explore three different deep-learning based architectures for multimodal sentiment classification, each improving upon the previous. Further, we evaluate these architectures with multiple datasets with fixed train/test partition. We also discuss some major issues, frequently ignored in multimodal sentiment analysis research, e.g., role of speaker-exclusive models, importance of different modalities, and generalizability. This framework illustrates the different facets of analysis to be considered while performing multimodal sentiment analysis and, hence, serves as a new benchmark for future research in this emerging field. We draw a comparison among the methods using empirical data, obtained from the experiments. In the future, we plan to focus on extracting semantics from visual features, cross-modal features and fusion.

期刊:arXiv, 2018年3月19日



5. A Benchmark Study on Sentiment Analysis for Software Engineering Research(对软件工程研究进行情感分析的基准研究)

作者:Nicole Novielli,Daniela Girardi,Filippo Lanubile

机构:University of Bari Aldo Moro

摘要:A recent research trend has emerged to identify developers' emotions, by applying sentiment analysis to the content of communication traces left in collaborative development environments. Trying to overcome the limitations posed by using off-the-shelf sentiment analysis tools, researchers recently started to develop their own tools for the software engineering domain. In this paper, we report a benchmark study to assess the performance and reliability of three sentiment analysis tools specifically customized for software engineering. Furthermore, we offer a reflection on the open challenges, as they emerge from a qualitative analysis of misclassified texts.

期刊:arXiv, 2018年3月17日



6.Sentiment Analysis of Code-Mixed Indian Languages: An Overview of SAIL_Code-Mixed Shared Task @ICON-2017(对代码混合的印度语言的情绪分析:对sail_code混合共享任务的概述)

作者:Braja Gopal Patra,Dipankar Das,Amitava Das

机构:University of Texas Health Science Center,Jadavpur University

摘要:Sentiment analysis is essential in many real-world applications such as stance detection, review analysis, recommendation system, and so on. Sentiment analysis becomes more difficult when the data is noisy and collected from social media. India is a multilingual country; people use more than one languages to communicate within themselves. The switching in between the languages is called code-switching or code-mixing, depending upon the type of mixing. This paper presents overview of the shared task on sentiment analysis of code-mixed data pairs of Hindi-English and Bengali-English collected from the different social media platform. The paper describes the task, dataset, evaluation, baseline and participant's systems.

期刊:arXiv, 2018年3月19日




原文发布于微信公众号 - 专知(Quan_Zhuanzhi)





0 条评论
登录 后参与评论




从年初起,几家国际大厂的开发者大会,无论是微软Build、Facebook F8还是稍后的Google I/O,莫不把“AI优先”的大旗扯上云霄。


用 Mathematica 生成正多面体链环









版权声明:本文为博主原创文章,未经博主允许不得转载。 https://blog.csdn.net/sinat_35512245/articl...


【专知-PyTorch手把手深度学习教程08】NLP-PyTorch: 用字符级RNN生成名字

【导读】主题链路知识是我们专知的核心功能之一,为用户提供AI领域系统性的知识学习服务,一站式学习人工智能的知识,包含人工智能( 机器学习、自然语言处理、计算机视...





接昨天的 系列一(可点击查看) 在系列一的教程中,我们想继续有关股票价格预测的主题,并赋予在系列1中建立的具有对多个股票做出响应能力的RNN。 为了区分不同价格...



算法 无源汇上下界可行流 ?  先强制流过l的流量 从s到每个正权点连流量为l的流量  从每个负权点向t连-l的流量 如果容量为0,则不连边 有源汇上下界最大流...

来自专栏Echo is learning

machine learning 之 Anomaly detection