Sentiment analysis of tech media articles using VADER package and co-occurrence analysis Sources with weights: Euractiv 5% The Conversation 5% Politico Europe 5 % IEEE Spectrum 5 % Techforge 5% Fastcompany 5% The Guardian (Tech) 12% Arstechnica 5% Reuters 5% Gizmodo 9% ZDNet 9% The Register 12% The Verge 9% TechCrunch 9% Methodology The sentiment analysis has been prepared using VADER*, an open-source lexicon and rule-based sentiment analysis tool. VADER is specifically designed for social media analysis, but can be also applied for other text sources. The sentiment lexicon was compiled using various sources (other sentiment data sets, Twitter etc.) and was validated by human input. The advantage of VADER is that the rule-based engine includes word-order sensitive relations and degree modifiers. As VADER is more robust in the case of shorter social media texts, the analysed articles have been divided into paragraphs. The analysis have been carried out for the social issues presented in the co-occurrence exercise. The process included the following main steps: The 100 most frequently co-occurring terms are identified for every social issue (using the co-occurrence methodology) The articles containing the given social issue and co-occurring term are identified The identified articles are divided into paragraphs Social issue and co-occurring words are removed from the paragraph The VADER sentiment analysis is carried out for every identified and modified paragraph The average for the given word pair is calculated for the final result Therefore, the procedure has been repeated for 100 words for all identified social issues. The sentiment analysis resulted in a compound score for every paragraph. The score is calculated from the sum of the valence scores of each word in the paragraph, and normalised between the values -1 (most extreme negative) and +1 (most extreme positive). Finally, the average is calculated from the paragraph results. Removal of terms is meant to exclude sentiment of the co-occurring word itself, because the word may be misleading, e.g. when some technologies or companies attempt to solve a negative issue. The neighbourhood's scores would be positive, but the negative term would bring the paragraph's score down. The presented tables include the most extreme co-occurring terms for the analysed social issue. The examples are chosen from the list of words with 30 most positive and 30 most negative sentiment. The presented graphs show the evolution of sentiments for social issues. The analysed paragraphs are selected the following way: The articles containing the given social issue are identified The paragraphs containing the social issue are selected for sentiment analysis *Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.