ADS Capstone Chronicles Revised

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well-received technique across academic and research contexts (Grisales et al., 2023). Our research will use topic modeling for sentiment analysis and excerpts of news articles to categorize underlying themes within different types of climate communication.

3.4 A Survey on Sentiment Analysis Methods, Applications, and Challenges Wankhade et al. (2022) discussed the various methodologies for sentiment analysis, such as lexicon-based, machine learning, and hybrid approaches. Lexicon-based sentiment analysis is an unsupervised technique and can be applied to many industries. The main disadvantage is domain dependence, but it can be overcome by the development of a domain-specific lexicon dictionary or the adaption of an existing library. The machine learning approach can be applied to unsupervised and supervised problems and includes commonly used algorithms such as naive Bayes, support vector machine, and logistic regression, among others. Hybrid approaches combine machine learning and lexicon-based methodologies and can be used for polarity recognition (Wankhade, 2022). To analyze news article excerpts, we will apply an unsupervised hybrid approach to identify overall sentiments for each news station. 3.5 Topic Modeling: Perspectives From a Literature Review Grisales et al. (2023) analyzed the evolution of topic modeling, the main areas in which it is applied, and recommended models for specific types of data. Their study had three main objectives: map scientific production using topic modeling, identify prominent authors and journal articles, and identify main applications and emerging trends. Four clusters were identified for the main applications of topic modeling: social media, information sciences, sentiment analysis, and short text. Furthermore, sentiment analysis and short text make up 24% and 26% of applications, respectively. Topic modeling is a versatile and

4 Methodology 4.1 DataOverview

The data consists of 90,863 instances of television news coverage of climate change across BBC News, CNN, MSNBC, and FOX News between July 2009 to January 2020. Data were obtained through the GDELT’s Television Explorer interface to the Internet Archive’s Television News Archive by using the following keywords: climate change , global warming , climate crisis , greenhouse gas , greenhouse gases , or carbon tax . Each observation contains the news snippet where climate change was mentioned, along with the time (in UTC Timezone), station, show, and URL link to a 15-second video clip of the mention on the Internet Archive website. In total, there were 25,593 mentions of climate change for MSNBC, 23,837 for FOX News, 22,693 for BBC News, and 18,740 for CNN. Notably, there was a spike in coverage toward the end of 2009, followed by a significant decline at the beginning of the decade (see Figure 1). Over time, there was a gradual increase in media attention, with an all-time high of 3,003 mentions in December 2019. Figure 1 Volume of News Coverage

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