Preparation of Improved Turkish DataSet for Sentiment Analysis in Social Media

A public dataset, with a variety of properties suitable for sentiment analysis [1], event prediction, trend detection and other text mining applications, is needed in order to be able to successfully perform analysis studies. The vast majority of data on social media is text-based and it is not possible to directly apply machine learning processes into these raw data, since several different processes are required to prepare the data before the implementation of the algorithms. For example, different misspellings of same word enlarge the word vector space unnecessarily, thereby it leads to reduce the success of the algorithm and increase the computational power requirement. This paper presents an improved Turkish dataset with an effective spelling correction algorithm based on Hadoop [2]. The collected data is recorded on the Hadoop Distributed File System and the text based data is processed by MapReduce programming model. This method is suitable for the storage and processing of large sized text based social media data. In this study, movie reviews have been automatically recorded with Apache ManifoldCF (MCF) [3] and data clusters have been created. Various methods compared such as Levenshtein and Fuzzy String Matching have been proposed to create a public dataset from collected data. Experimental results show that the proposed algorithm, which can be used as an open source dataset in sentiment analysis studies, have been performed successfully to the detection and correction of spelling errors.Comment: Presented at CMES201

Spatiotemporal Variation in Emotional Responses to 2017 Terrorist Attacks in London Using Twitter Data

Terrorist attacks have a significant impact on human lives. This study examined emotional responses after the terrorist attacks in London in March and June of 2017, respectively. This research extracted tweets related to the two attacks by developing a Python tool interacting with the Twitter Application Program Interface (API). The tweets were analyzed for its negative emotion expression such as sadness. This study then analyzed these negative tweets using the space-time permutation model in SatScan and assessed their variation in space and time. Results suggested two significant clusters of negative tweets after the first attack. These clusters located in the metropolitan area of London and between Manchester and Liverpool within ten days of the attack. The findings may contribute to quick surveillance of emotional responses on the Twitter users

Service quality monitoring in confined spaces through mining Twitter data

Promoting public transport depends on adapting effective tools for concurrent monitoring of perceived service quality. Social media feeds, in general, provide an opportunity to ubiquitously look for service quality events, but when applied to confined geographic area such as a transport node, the sparsity of concurrent social media data leads to two major challenges. Both the limited number of social media messages--leading to biased machine-learning--and the capturing of bursty events in the study period considerably reduce the effectiveness of general event detection methods. In contrast to previous work and to face these challenges, this paper presents a hybrid solution based on a novel fine-tuned BERT language model and aspect-based sentiment analysis. BERT enables extracting aspects from a limited context, where traditional methods such as topic modeling and word embedding fail. Moreover, leveraging aspect-based sentiment analysis improves the sensitivity of event detection. Finally, the efficacy of event detection is further improved by proposing a statistical approach to combine frequency-based and sentiment-based solutions. Experiments on a real-world case study demonstrate that the proposed solution improves the effectiveness of event detection compared to state-of-the-art approaches

Tracing Public Opinion Propagation and Emotional Evolution Based on Public Emergencies in Social Networks

Social network has become the main communication platform for public emergencies, and it has also made the public opinion influence spread more widely. How to effectively obtain public opinions from it to guide the healthy development of the society is an important issue that the government and other functional departments are concerned about. However, the interaction and evolution mechanism between the subject and the environment in the public opinion propagation is complicated, and the public and media attention and reaction to the incident are closely linked with the progress of the incident disposal. And public mining corpus has some shortcomings in the distribution of emotional classification. Only the timely update of artificial rules and emotional dictionary resources, it can handle new text data well. In fact, from the perspective of public opinion propagation, this paper built the network matrix between Internet users through the forwarding relationship, and used the social network analysis method and the emotion mining analysis technology to study the interaction and evolution mechanism between the subject and the environment in the public opinion propagation, and it studied the role of users in the emotional propagation of social networks. This paper proposed a sentiment analysis method on the micro-blog platform, which expanded the emotional dictionary and took sentence and emoticon and sentence patterns into account, which improved the accuracy of positive and negative classifications and emotional polarity analysis of the micro-blog

Building a Test Collection for Significant-Event Detection in Arabic Tweets

With the increasing popularity of microblogging services like Twitter, researchers discov- ered a rich medium for tackling real-life problems like event detection. However, event detection in Twitter is often obstructed by the lack of public evaluation mechanisms such as test collections (set of tweets, labels, and queries to measure the eectiveness of an information retrieval system). The problem is more evident when non-English lan- guages, e.g., Arabic, are concerned. With the recent surge of signicant events in the Arab world, news agencies and decision makers rely on Twitters microblogging service to obtain recent information on events. In this thesis, we address the problem of building a test collection of Arabic tweets (named EveTAR) for the task of event detection. To build EveTAR, we rst adopted an adequate denition of an event, which is a signicant occurrence that takes place at a certain time. An occurrence is signicant if there are news articles about it. We collected Arabic tweets using Twitter's streaming API. Then, we identied a set of events from the Arabic data collection using Wikipedias current events portal. Corresponding tweets were extracted by querying the Arabic data collection with a set of manually-constructed queries. To obtain relevance judgments for those tweets, we leveraged CrowdFlower's crowdsourcing platform. Over a period of 4 weeks, we crawled over 590M tweets, from which we identied 66 events that cover 8 dierent categories and gathered more than 134k relevance judgments. Each event contains an average of 779 relevant tweets. Over all events, we got an average Kappa of 0.6, which is a substantially acceptable value. EveTAR was used to evalu- ate three state-of-the-art event detection algorithms. The best performing algorithms achieved 0.60 in F1 measure and 0.80 in both precision and recall. We plan to make our test collection available for research, including events description, manually-crafted queries to extract potentially-relevant tweets, and all judgments per tweet. EveTAR is the rst Arabic test collection built from scratch for the task of event detection. Addi- tionally, we show in our experiments that it supports other tasks like ad-hoc search

Society, History and Education: dialogues from a disciplinary perspective

We are pleased to present to the entire academic community and the general public the following work, which contains recent research results of a group of teachers from the Faculty of Educational Sciences in areas such as pedagogy, communication, technology and history. This publication is the result of the work coordinated by the Vice Rector's Office for Research, Innovation and Extension of the UTP, with the support of the Faculty of Education Sciences, through the realization of the First Conference on Social Appropriation of Knowledge held in 2022, in order to reach a wider field of dissemination of local research. In the first chapter "Sentiment analysis on Twitter about mobile learning" by professors Rosa María Guilleumas García and Hernán Gil Ramírez, a study of tweets about mobile learning is presented. To do so, the authors combined several techniques of social network analysis, text mining and sentiment analysis, using NodeXL software, specialized in network examination and visualization. Among their results, they highlight the great predominance of positive tweets over negative ones in this field of study, and at the same time point out that, in the analyzed tweets, the most used words were learning, mobile, app, machine, mlearning and education. In second place, there is the chapter "The institutional educational project. An opportunity for reflection and transformation of the Colombian university" by teachers Martha Cecilia Gutiérrez Giraldo and Carolina Franco Ossa, which arises from the reflection on university autonomy and its internal exercise in the construction of its Institutional Educational Projects (PEI). Thus, the main purpose of this work is to identify the relevant facts that have marked the academic life of the UTP since its creation in 1958 until 2015, in which the different strata of the university community (teachers, students, administrators, managers, graduates and the social sector) participated through a participatory action research process. The results show that since its creation, the University has updated its academic and management policies in accordance with the regulations in force in each period, and that, at the same time, as mentioned by the authors, the institutional processes of self-reflection and projection of the academic life of the UTP should be strengthened through the culture of academic and democratic participation, supported by the collaborative work of the university communityCONTENT Presentation...................................................................................................................5 CHAPTER ONE Twitter sentiment analysis on mobile learning ...............................................................9 Rosa María Guilleumas García y Hernán Gil Ramírez CHAPTER TWO The institutional educational project. an opportunity for reflection and transformation of the Colombian university.................................................................37 Martha Cecilia Gutiérrez Giraldo y Carolina Franco Ossa CHAPTER THREE The Ridway´s photographies: typologies of portraiture in Pereira, Colombia .............59 Johana Guarín Medina CHAPTER FOUR Information society. Political disputes and disciplinary openings................................91 Andrés Camilo Agudelo Vergara CHAPTER FIVE State and internal borders in the 19th century: the Quindío mountain in central western Colombia .......................................................................1