Sentiment analysis and classification of Forest Fires in Indonesia

Twitter is a well-known social media platform since it allows users to retweet, leave comments, exchange the latest information, and even find out about forest fires. However, no one has processed Twitter data in the form of the topic of forest fires. Despite the fact that this information is incredibly important for determining how much people care about sharing this knowledge and this phenomenon. Hence, one of the efforts in managing Twitter data in the form of text is using NLP (Natural Language Processing) which is now starting to be widely discussed. In addition, the use of word weighting utilizing Vader will also be used in this process. Furthermore, the use classifying process is conducted using 3 kinds of algorithms including Naïve Bayes, Random Forest and SVM (Support Vector Machine). The results of this study, the accuracy obtained from each method has not reached 90%. The Precision, Recall and F1-Score values have also not reached 90%

Machine Learning-Based Models for Assessing Impacts Before, During and After Hurricane Events

Social media provides an abundant amount of real-time information that can be used before, during, and after extreme weather events. Government officials, emergency managers, and other decision makers can use social media data for decision-making, preparation, and assistance. Machine learning-based models can be used to analyze data collected from social media. Social media data and cloud cover temperature as physical sensor data was analyzed in this study using machine learning techniques. Data was collected from Twitter regarding Hurricane Florence from September 11, 2018 through September 20, 2018 and Hurricane Michael from October 1, 2018 through October 18, 2018. Natural language processing models were developed to demonstrate sentiment among the data. Forecasting models for future events were developed for better emergency management during extreme weather events. Relationships among data were explored using social media data and physical sensor data to analyze extreme weather events as these events become more prevalent in our lives. In this study, social media sentiment analysis was performed that can be used by emergency managers, government officials, and decision makers. Different machine learning algorithms and natural language processing techniques were used to examine sentiment classification. The approach is multi-modal, which will help stakeholders develop a more comprehensive understanding of the social impacts of a storm and how to help prepare for future storms. Of all the classification algorithms used in this study to analyze sentiment, the naive Bayes classifier displayed the highest accuracy for this data. The results demonstrate that machine learning and natural language processing techniques, using Twitter data, are a practical method for sentiment analysis. The data can be used for correlation analysis between social sentiment and physical data and can be used by decision makers for better emergency management decisions

Сервіс аналізу емоційного забарвлення текстових повідомлень

У даному бакалаврському дипломному проєкті було розроблено сервіс для аналізу емоційного забарвлення текстових повідомлень для української та англійської мов. Основна задача сервісу полягає у сентимент-аналізі мотиваційних листів, звітів, офіційних документів, коментарів тощо. Це робиться для того, щоб надати оцінювачу можливість зосередитися на найважливіших висновках і, таким чином, полегшити робоче навантаження та звільнити себе від трудомісткої та нудної роботи. У проєкті також було оглянуто, розроблено та порівняно найпопулярніші моделі класифікації з використанням машинного та глибинного навчання. А найкращі моделі були використані для роботи сервісу. Програмна реалізація моделей класифікації була розроблена на мові Python. Користувацький інтерфейс, у вигляді веб-сервісу, було створено за допомогою веб-фреймворку Django.In the given bachelor's thesis, a service for the analysis of the sentimental component of text messages for the Ukrainian and English languages has been developed. The main task of the service lies in the sentiment analysis of motivational letters, reports, official documents, comments, etc. It is performed to allow the assessor to focus on the most important conclusions and thus facilitate the workload and free themselves from time-consuming and tedious work. In the thesis, the most popular machine and deep learning classification models were reviewed, developed, and compared. The best models were used for the service performance. The software implementation of classification models was developed in Python. The user interface in the form of a web service was created using the Django web framework

Sentiment analysis on Twitter data using machine learning

In the world of social media people are more responsive towards product or certain events that are currently occurring. This response given by the user is in form of raw textual data (Semi Structured Data) in different languages and terms, which contains noise in data as well as critical information that encourage the analyst to discover knowledge and pattern from the dataset available. This is useful for decision making and taking strategic decision for the future market. To discover this unknown information from the linguistic data Natural Language Processing (NLP) and Data Mining techniques are most focused research terms used for sentiment analysis. In the derived approach the analysis on Twitter data to detect sentiment of the people throughout the world using machine learning techniques. Here the data set available for research is from Twitter for world cup Soccer 2014, held in Brazil. During this period, many people had given their opinion, emotion and attitude about the game, promotion, players. By filtering and analyzing the data using natural language processing techniques, and sentiment polarity has been calculated based on the emotion word detected in the user tweets. The data set is normalized to be used by machine learning algorithm and prepared using natural language processing techniques like Word Tokenization, Stemming and lemmatization, POS (Part of speech) Tagger, NER (Name Entity recognition) and parser to extract emotions for the textual data from each tweet. This approach is implemented using Python programming language and Natural Language Toolkit (NLTK), which is openly available for academic as well as for research purpose. Derived algorithm extracts emotional words using WordNet with its POS (Part-of-Speech) for the word in a sentence that has a meaning in current context, and is assigned sentiment polarity using ‘SentWordNet’ Dictionary or using lexicon based method. The resultant polarity assigned is further analyzed using Naïve Bayes and SVM (support vector Machine) machine learning algorithm and visualized data on WEKA platform. Finally, the goal is to compare both the results of implementation and prove the best approach for sentiment analysis on social media for semi structured data.Master of Science (MSc) in Computational Science

Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis

We propose a novel method for counting sentiment orientation that outperforms supervised learning approaches in time and memory complexity and is not statistically significantly different from them in accuracy. Our method consists of a novel approach to generating unigram, bigram and trigram lexicons. The proposed method, called frequentiment, is based on calculating the frequency of features (words) in the document and averaging their impact on the sentiment score as opposed to documents that do not contain these features. Afterwards, we use ensemble classification to improve the overall accuracy of the method. What is important is that the frequentiment-based lexicons with sentiment threshold selection outperform other popular lexicons and some supervised learners, while being 3–5 times faster than the supervised approach. We compare 37 methods (lexicons, ensembles with lexicon’s predictions as input and supervised learners) applied to 10 Amazon review data sets and provide the first statistical comparison of the sentiment annotation methods that include ensemble approaches. It is one of the most comprehensive comparisons of domain sentiment analysis in the literature

The Impact of Community Cohesion on Crime

Community cohesion generally acts to increase the safety of communities by increasing informal guardianship, and enhancing the work of formal crime prevention organisations. Understanding the dynamics of local social interactions is essential for community building. However, community cohesion is difficult to empirically quantify, because there are no obvious and direct indicators of community cohesion collected at population levels within official datasets. A potentially more promising alternative for estimating community cohesion is through the use of data from social media. Social media offers an opportunity for exploring networks of social interactions in a local community. This research will use social media data to explore the impact of community cohesion on crime. Sentiment analysis of tweets can help to uncover patterns of community mood in different areas. Modelling of community engagement on Facebook is useful for understanding patterns of social interactions and the strength of social networks in local communities. The central contribution of this thesis is the use of new metrics that estimate popularity, commitment and virality known as the PCV indicators for quantifying community cohesion on social media. These metrics, combined with diversity statistics constructed from “traditional” Census data, provide a better correlate of community cohesion and crime. To demonstrate the viability of this novel method for estimating the impact of community cohesion, a model of community engagement and burglary rates is constructed using Leeds community areas as an example. By examining the diversity of different community areas and strength of their social networks, from traditional and new data sources; it was found that stability and strong social media engagement in a local area are associated with lower burglary rates. The proposed new method can provide a better alternative for estimating community cohesion and its impact on crime. It is recommended that policy planning for resource allocation and community building needs to consider social structure and social networks in different communities