Matsuo m abst

Hadoop is an open-source software framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. This document analyzes using Hadoop to process a large newspaper database, taking advantage of Hadoop's ability to handle large amounts of unstructured and semi-structured data through distributed computing. The analysis explores how Hadoop can scale to process the entire newspaper database and extract useful information and insights.

Appel Agent Administratif ref : 051/15

Carsat Centre Ouest

Mejlväxlingen mellan Google och Språkrådet, del 6 och 7

SvD Digitala Medier

Eclipse de luna más corto del siglo

marquial

Este sábado ocurrirá un eclipse de luna que será el más corto del siglo XXI, durando solo 5 minutos. Aunque será un eclipse total, en Colombia solo se podrá apreciar de manera parcial poco antes del amanecer, ya que la sombra de la Tierra comenzará a cubrir la Luna a las 5:16 am pero la luz del Sol hará que se desvanezca rápidamente. El eclipse será visible en algunas partes de América del Norte, el Pacífico, Nueva Zelanda, Australia y el este de Asia, pero

Este sábado habrá un eclipse de luna que será el más corto del siglo XXI, durando solo 5 minutos. Aunque será un eclipse total, en Colombia solo se podrá apreciar de manera parcial poco antes del amanecer, ya que la sombra de la Tierra comenzará a cubrir la Luna a las 5:16 am pero la luz del Sol hará que se desvanezca rápidamente. El eclipse forma parte de una serie de cuatro eclipses lunares conocida como tétrada, que comenzó el año pasado y terminará en septiemb

Ebata m abst

This master's thesis from 2010 analyzes user comments on videos using the tf-idf method to interactively suggest related videos. The author treats all comments on a single video as a document and calculates feature vectors for each document. A user's "comment tendency vector" is synthesized from videos they've watched. Related videos are identified by calculating similarity between this vector and feature vectors of other videos. The author has one published paper on visualizing research outputs using ExtJS and Google Chart.

20150410132155Glen L. Loveland

Risico's analyse

Wilma Vervoort

Het inventariseren en beheren/beheersen van risico's op een effectieve en democratische wijze. Alle stakeholders kunnen meedoen wanneer u de Mobiele Gesprekskamer van Interactive Acts gebruikt. On site of online. Deelnemers voeren individueel (en anoniem) al dan niet tegelijkertijd de risico's in die zij voorzien. Dat hangt af of er een gelijktijdige bijeenkomst wordt georganiseerd of een doorlopende. Aan u de keus! Men kan op elkaars input reageren. De risico's kunnen geprioriteerd worden op basis van een of meer criteria. Zo breng je snel zaken in kaart en kun je adequaat besluiten nemen. Op basis van feiten in plaats van aannames. Mail naar info@interactiveacts.com voor meer informatie.

Social Sensor for Real Time Event Detection

Social sites play an important role in our day to day life. Peoples are spending at most of the time on social networking sites and for this reason social networking sites have received a more attention in last few years. Social networking websites have received greater attention in the past few years .Twitter is one of the most popular networking site which received attention from peoples by providing its real time nature. The important characteristic of our system is its real-time nature. We create a system to collects the tweets from various groups of users and detect real time events like earthquake, traffic etc. To detect real time events, we build a classifier of comments based on features such as the keywords in a comments, the number of words. With the help of comments we will approximately find out the location of the target event. We assume each user as a sensor and apply priority base algorithm which is used for tweet analysis. To resolve the traffic congestion problem, we have to consider the volume of the traffic, traffic speed, road occupancy etc. Classify tweets into a positive and negative class. Produce a probabilistic model for event detection. We create group of users on the basis of their interest such as peoples from traffic police department are post the tweets related to traffic event, also peoples form whether department are post their tweets related to flood etc. Each tweet is associated with user group, tweet, time and location. By processing time and location information, we can detect the real time events.

Detection and Analysis of Twitter Trending Topics via Link-Anomaly Detection

This paper involves two approaches for finding the trending topics in social networks that is key-based approach and link-based approach. In conventional key-based approach for topics detection have mainly focus on frequencies of (textual) words. We propose a link-based approach which focuses on posts reflected in the mentioning behavior of hundreds users. The anomaly detection in the twitter data set is carried out by retrieving the trend topics from the twitter in a sequential manner by using some API and corresponding user for training, then computed anomaly score is aggregated from different users. Further the aggregated anomaly score will be feed into change-point analysis or burst detection at the pinpoint, in order to detect the emerging topics. We have used the real time twitter account, so results are vary according to the tweet trends made. The experiment shows that proposed link-based approach performs even better than the keyword-based approach.

IRJET- Effective Countering of Communal Hatred During Disaster Events in Soci...

IJET - International Journal of Engineering and Techniques

This document summarizes a research paper that aims to effectively counter communal hatred during disaster events on social media. It uses machine learning techniques to analyze tweets and classify them based on parameters like offensive, hatred, or neither. Tweets are collected using Twitter's API and preprocessed. A supervised machine learning algorithm (Support Vector Machine) is trained on manually labeled tweet data to classify new tweets. The results are visualized in a pie chart graph displaying the percentage of tweets containing offensive, hatred, or neutral words. The goal is to reduce the spread of communal hate speech on social media during disasters.

[IJET V2I4P9] Authors: Praveen Jayasankar , Prashanth Jayaraman ,Rachel Hannah

Sentiment Analysis is the process of finding the sentiments from different classes of words. Generally speaking, sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document. The attitude may be his or her judgment or evaluation, affective state, or the intended emotional communication. In this case, ‘tweets’! Given a micro-blogging platform where official, verified tweets are available to us, we need to identify the sentiments of those tweets. A model must be constructed where the sentiments are scored, for each product individually and then they are compared with, diagrammatically, portraying users’ feedback from the producers stand point. There are many websites that offer a comparison between various products or services based on certain features of the article such as its predominant traits, price, and its welcome in the market and so on. However not many provide a juxtaposing of commodities with user review as the focal point. Those few that do work with Naïve Bayes Machine Learning Algorithms, that poses a disadvantage as it mandatorily assumes that the features, in our project, words, are independent of each other. This is a comparatively inefficient method of performing Sentiment Analysis on bulk text, for official purposes, since sentences will not give the meaning they are supposed to convey, if each word is considered a separate entity. Maximum Entropy Classifier overcomes this draw back by limiting the assumptions it makes of the input data feed, which is what we use in the proposed system.

Sub1557

International Journal of Science and Research (IJSR)

This document summarizes a research paper on opinion mining from Twitter data. It discusses the challenges of sentiment analysis on short Twitter posts, including named entity recognition, anaphora resolution, parsing, and detecting sarcasm. It also reviews several papers on related topics, such as frameworks for Twitter opinion mining using classification techniques, using Twitter as a corpus for sentiment analysis, and analyzing opinions during the 2012 Korean presidential election on Twitter. Overall, it covers key techniques in opinion mining like identifying opinion targets and orientation. It proposes future work to develop a web application to compare Twitter opinion mining performance and use supervised learning to improve accuracy.

Applying Clustering Techniques for Efficient Text Mining in Twitter Data

ijbuiiir1

Knowledge is the ultimate output of decisions on a dataset. The revolution of the Internet has made the global distance closer with the touch on the hand held electronic devices. Usage of social media sites have increased in the past decades. One of the most popular social media micro blog is Twitter. Twitter has millions of users in the world. In this paper the analysis of Twitter data is performed through the text contained in hash tags. After Preprocessing clustering algorithms are applied on text data. The different clusters formed are compared through various parameters. Visualization techniques are used to portray the results from which inferences like time series and topic flow can be easily made. The observed results show that the hierarchical clustering algorithm performs better than other algorithms.

A Paper on Web Data Segmentation for Terrorism Detection using Named Entity R...

This paper proposes a system to detect web pages and tweets that may promote terrorism using data mining and machine learning techniques. It involves extracting text data from web pages using DOM trees, segmenting tweets and web data using named entity recognition, clustering segmented data using k-means, and applying techniques like SIFT and pattern matching to detect potentially terrorist content. The aim is to automatically flag such web pages and tweets for human review to help curb the spread of terrorism online.

Categorize balanced dataset for troll detection

As we know cyber bullying is increasing day by day and Cyber troll is one of the cyber-aggressive actions that is not much different from cyberbullying in online abuse so that the victims feel uncomfortable. One of the most used social media platforms in which cyber trolling frequently happens is Twitter. Basically, it is found that during an investigation of cyberbullying cases a lot of information gathered is false which aims to give discomfort, hatred and waste lots of time. So, it is necessary to classify between cyberbullying tweets and normal tweets on twitter. There has already been research on classification of cyberbullying tweets and normal tweets using the Support vector machine (SVM) algorithm. But the drawback of the system is that it only gives 63.83% of accuracy. Firstly, we can improve the accuracy of the system by using the Recurrent Neural Network (RNN) And Secondly, for balancing the dataset we will be using Synthetic Minority Over-sampling Technique (SMOTE). We believe that using these techniques we will be able to increase the accuracy of the previous proposed.

The Identification of Depressive Moods from Twitter Data by Using Convolution...

The document presents research on identifying depressive moods on Twitter using a convolutional neural network model. Key points: - A CNN model was developed using text and emoji representations from Twitter data to predict depressive moods. - The model achieved 88% accuracy on the test data, demonstrating it could correctly classify sentiments most of the time. - The model was more successful at identifying negative sentiments than positive ones, based on higher precision and recall rates for negative classes. - Integrating both text and emoji data enhanced the model's performance, showing potential for more nuanced sentiment analysis.

Tweet Summarization and Segmentation: A Survey

The document summarizes various techniques for tweet summarization and segmentation, and discusses how the particle swarm optimization (PSO) algorithm can be used for tweet clustering. It provides an overview of 8 papers that propose different approaches for tweet summarization using techniques like graph clustering, keyword graphs, and segmentation. It also analyzes these techniques and compares the performance of PSO to other clustering algorithms like K-means. The analysis shows that methods combining K-means and PSO have higher accuracy than K-means alone, and that PSO performs better than K-means for real-time tweet clustering, achieving an accuracy of 90.6% versus 62% for K-means.

Enhancing prediction of user stance for social networks rumors

IJECEIAES

The spread of social media has led to a massive change in the way information is dispersed. It provides organizations and individuals wider opportunities of collaboration. But it also causes an emergence of malicious users and attention seekers to spread rumors and fake news. Understanding user stances in rumor posts is very important to identify the veracity of the underlying content as news becomes viral in a few seconds which can lead to mass panic and confusion. In this paper, different machine learning techniques were utilized to enhance the user stance prediction through a conversation thread towards a given rumor on Twitter platform. We utilized both conversation thread features as well as features related to users who participated in this conversation, in order to predict the users’ stances, in terms of supporting, denying, querying, or commenting (SDQC), towards the source tweet. Furthermore, different datasets for the stance-prediction task were explored to handle the data imbalance problem and data augmentation for minority classes was applied to enhance the results. The proposed framework outperforms the state-of-the-art results with macro F1-score of 0.7233

IRJET- Improved Real-Time Twitter Sentiment Analysis using ML & Word2Vec

This document discusses improving real-time Twitter sentiment analysis using machine learning and Word2Vec. It begins by introducing sentiment analysis and its importance for product analysis and business. Next, it describes extracting Twitter data using APIs, preprocessing it, and applying machine learning algorithms like Naive Bayes, logistic regression, and decision trees to classify tweets as expressing positive, negative or neutral sentiment. It also reviews literature on using techniques like linguistic analysis and ensemble models to improve sentiment analysis from social media sources.

F017433947

IOSR Journals

This document summarizes a research paper that proposes using a logistic regression classifier trained with stochastic gradient descent to predict Twitter users' personalities from their tweets. It begins with an abstract of the paper and an introduction on personality prediction from social media. It then provides more detail on the anatomy of the research, including defining personality prediction from Twitter, its applications, and the general process of using machine learning for the task. Next, it reviews several previous studies on personality prediction from Twitter and social networks, noting their approaches, findings and limitations. It identifies remaining research gaps, such as the need for improved linguistic analysis of tweets and more robust/scalable predictive models. Finally, it proposes using a logistic regression classifier as the personality prediction model to address

Filter unwanted messages from walls and blocking non legitimate users in osn

1. The document presents a system to filter unwanted messages from user walls in online social networks. It aims to give users more control over the content that appears on their walls. 2. A machine learning classifier is used to automatically label messages by category. Users can then specify filtering rules to block certain categories or keywords from appearing. 3. The system also implements a blacklist to temporarily or permanently block users who frequently post unwanted content, as determined by filtering rules and a threshold.

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Sentiment Analysis on Twitter data using Machine Learning

This document discusses sentiment analysis on Twitter data using machine learning. It describes how Twitter is used to express opinions and sentiments, and how sentiment analysis can be used to extract user sentiments from tweets. The document outlines how Python libraries like TextBlob and Tweepy can be used to connect to the Twitter API, retrieve tweets, clean the data, perform sentiment analysis using a sentiment dictionary, and visualize the results. It presents the results of analyzing tweets about a particular topic, finding most tweets had a neutral sentiment, with more negative than positive. The conclusion discusses the improvements in models over time but remaining challenges from data variety and informal language on Twitter.

IRJET - Implementation of Twitter Sentimental Analysis According to Hash Tag

This document proposes a model for analyzing sentiment from tweets using hashtags. It involves collecting tweets, preprocessing the data by removing URLs and stopwords, training a classifier using Naive Bayes, and classifying tweets as positive, negative, or neutral. Hashtags are also classified to help organize tweets by topic. The proposed system is intended to help large companies understand public sentiment about their brands by analyzing tweets in real-time.

MINING OPINIONS ABOUT TRAFFIC STATUS USING TWITTER MESSAGES

The document describes a system for mining opinions from tweets about traffic status. Around 5000 traffic-related tweets were collected and preprocessed by removing stop words and punctuation. The tweets were manually labeled as expressing positive (p) or negative (n) opinions. Various classifiers were trained on the labeled data and evaluated. The top 7 classifiers were combined into an ensemble model, which achieved an F-measure of 87.15% for classifying tweets in the test set, indicating the system is effective at mining opinions on traffic status from tweets.

Ijciet 08 02_024

The document describes a study that aimed to develop a system for mining opinions from tweets about traffic status. Over 5,000 traffic-related tweets were collected and preprocessed by removing stop words and punctuation. The tweets were manually labeled as expressing either positive (p) or negative (n) sentiment. Various classifiers were trained on 80% of the labeled data and validated. The top 7 performing classifiers were combined into an ensemble model, which achieved an F-measure of 87.15% when classifying the remaining 20% of tweets, indicating the system is effective at mining traffic sentiment from tweets.

IRJET- Identification of Prevalent News from Twitter and Traditional Media us...

This document describes a study that uses community detection models to identify prevalent news topics discussed on both Twitter and traditional media like BBC. It collects tweets and news articles about sports over a one-month period. Keywords are extracted from the data and a graph is constructed to represent relationships between words. Three community detection models - Girvan-Newman clustering, CLIQUE, and Louvain - are used to cluster similar content and detect communities of keywords representing news topics. The number of unique Twitter users engaged with each topic is also calculated to rank topics by user attention. The goal is to analyze how information is distributed between social and traditional media and identify emerging topics with low coverage in traditional sources.

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公開URL：https://arxiv.org/pdf/2307.09288.pdf 出典：Touvron Hugo, et al.: Llama 2: Open foundation and fine-tuned chat models, arXiv preprint arXiv:2307.09288 (2023) 概要：70億から700億のパラメータを持つ大規模言語モデル（LLM）の事前学習であるLlama 2を開発し、リリースしました。Llama 2-Chatと呼ばれるファインチューニングされたLLMは、対話のユースケースに最適化されています。提案モデルは、検証したほとんどのベンチマークにおいて、オープンソースのチャットモデルを凌駕しており、有用性と安全性に関する人間による評価に基づいて、クローズドソースのモデルの適切な代替となる可能性があります。コミュニティが我々の研究を基に、LLMの責任ある開発に貢献できるようにすることも目的にあります。

DLゼミ: ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation

公開URL：https://proceedings.neurips.cc/paper_files/paper/2022/file/fbb10d319d44f8c3b4720873e4177c65-Paper-Conference.pdf 出典：Yufei Xu, Jing Zhang, Qiming Zhang, Dacheng Tao : ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation, Advances in Neural Information Processing Systems 35 (NeurIPS 2022), pp. 38571-38584 (2022) 概要：姿勢推定はコンピュータビジョンの1タスクとして知られています。本論文ではプレーンなVision Transformerを使った姿勢推定手法「ViTPose」を提案します。ViTPoseは非階層的なVision Transformerを特徴抽出のために使用し、100Mから1Bのパラメータにスケールアップ可能です。このモデルは事前学習や、複数のポーズタスク対応などにおいて柔軟性を持ち、大規模モデルの知識を小規模モデルに転送することも可能です。実験結果は、ViTPoseがMS COCO Keypoint DetectionベンチマークにおいてSoTAを達成しました。

Voyager: An Open-Ended Embodied Agent with Large Language Models

公開URL：https://arxiv.org/abs/2305.16291 出典：Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, Anima Anandkumar : Voyager: An Open-Ended Embodied Agent with Large Language Models, arXiv: 2305.16291 (2023) 概要：本論文では、Minecraftの世界を持続的に探索し、多様なスキルを獲得し、新たな発見を人間の介入なく行う最初の大規模言語モデル（LLM）を駆使した具現化エージェントであるVoyagerを紹介する。Voyagerは、探索を最大化する自動カリキュラム、複雑な行動を格納・取得するためのスキルライブラリ、そして環境のフィードバック、実行エラー、プログラム改善のための自己確認を組み込む新たな反復的なプロンプトメカニズムの3つの主要なコンポーネントから成り立つ。

DLゼミ: Ego-Body Pose Estimation via Ego-Head Pose Estimation

公開URL：https://arxiv.org/abs/2212.04636 出典：Jiaman Li, C. Karen Liu, Jiajun Wu : Ego-Body Pose Estimation via Ego-Head Pose Estimation, arXiv preprint arXiv:2212.04636 (2022) 概要：人間の行動理解やVR/ARへの応用において、一人称映像から人の3次元姿勢 (3D human motion)を推定することは重要である。提案手法EgoEgoは、中間表現として頭部モーションを採用した新たな手法である。問題を2分割したことにより、既存のデータセットが活用でき、他のSoTA手法より優れた性能を示した。さらにベンチマークのための一人称映像と人の3次元推定データセットARESを開発した。

ReAct: Synergizing Reasoning and Acting in Language Models

公開URL：https://arxiv.org/abs/2210.03629 出典：Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, Yuan Cao : ReAct: Synergizing Reasoning and Acting in Language Models, arXiv: 2210.03629 (2022) 概要：本論文では大規模言語モデルにおいて推論(Reasoning)と行動(Acting)を組合わせるprompt手法であるReActを提案した。知識集約型の推論タスク（QAタスク・事実検証タスク）と意思決定タスク（テキストゲーム、ウェブナビゲーション）でReActの性能を評価した。推論タスクにおいてReActはWikipedia APIとの対話によりChain of ThoughtのHallucinationを軽減し、意思決定タスクにおいては模倣学習や強化学習による手法を上回る性能を示した。

形態素解析を用いた帝国議会議事速記録の変遷に関する研究

対話形式議事録は，口語での会話内容が記録されており，言語研究において重要な資料となる．対話形式議事録の1つである帝国議会議事速記録は，記録期間が長く分量も多いという特徴があり，近代における言語表現の変化を分析するのに適した資料であるといえる．このようなテキストデータに対して分析の第一段階である表現抽出を自動で行う手法については知見が少なく，抽出作業のほとんどが手作業で行われているのが現状である．大規模なデータに対して手作業での該当表現の抽出を行うのには多大な労力がかかり，見逃しも発生しやすく，分析を行う上でのボトルネックとなる．本研究では，抽出段階の自動化を目的とし，可能の意味を表す可能表現について，品詞の組み合わせマッチングによる自動抽出を試みた．さらに，可能表現の出現頻度や変遷と会議の話題の関係性を分析するための先駆けとして，重要単語抽出による話題の自動タグ付けを行った．話題タグの自動付与においては，TF-IDFを用いたシンプルな特徴単語の抽出を試みた．可能表現の抽出においては，文型に注目した品詞のパターンマッチングによる抽出を試みた．

【卒業論文】深層生成モデルを用いたユーザ意図に基づく衣服画像の生成に関する研究

灯油タンク内の液面高計測を用いた灯油残量推定システムに関する研究

近年，家庭への灯油配送においてリモートセンシング化が進んでおり，灯油タンク内の残量計測が可能な通信型レーザセンサの開発が進んでいる．しかし，屋外にある灯油タンクにセンサを設置するため計測環境が不安定で，コスト面から導入可能なデバイスの性能にも制約があるため，センサの故障や異常値の計測が多く発生する．このような背景を踏まえて，本論文では，センサを管理・運用するための運用手法の確立を目的として，2つの異常検知手法と1つの残量推定手法を提案し，実データを用いた有効性の検証を行う．

深層自己回帰モデルを用いた俳句の生成と評価に関する研究

論文では，人工知能による創作の第一歩として俳句，その中でも特に一般的な有季定型句を対象とし，深層学習モデルを用いた生成と評価を行う. 既存の俳句や日本語テキストを用いてモデルを学習し，俳句生成器を構築する. また，作成したモデルの俳句生成器としての性能を評価するために，俳人による評価済みの俳句からなる評価用データセットを作成して，有季定型句の生成における各モデルの性能を比較する. 評価用データセットの作成においてはまず，有季定型句の評価項目として句会における評価方式と俳句内の句節のつながりに着目し，これらを段階的評価の階層構造として定義した. 定義した階層構造に基づくラベル付を複数の俳人に依頼し，モデル間の性能評価に用いる. また，各モデルが生成した文字列に対するアンケート調査を行い，現状のモデルの到達点を明らかにする.

競輪におけるレーティングシステムを用いた予想記事生成に関する研究

本研究では，競輪のレース結果予測の初心者が車券を購入する際の意思決定を補助することを目的として，レース結果を予測して，予測結果を用いた選手紹介文を生成するシステムを構築する．選手の戦績から的確に反映する特徴量を算出する手法として，多数人の同時対戦を考慮した拡張イロレーティングシステムを提案して，競輪のレース結果予測に適用する．実際の競輪のレース結果を用いた検証実験において，既存のレース結果予測手法に比べて，拡張イロレーティングシステムを用いることで予測精度を向上可能であることを確認した．さらに，拡張イロレーティングシステムを紹介文生成に用いることで，既存の手法に比べて，初心者の車券購入により有用な文章を生成可能であることを示す．

【卒業論文】B2Bオークションにおけるユーザ別入札行動予測に関する研究