Sentiment Analysis: Daily Tweet

Below outlines process of extracting tweets, cleaning tweets, evaluating sentiment scores for each tweet and as well as calculating 10 emotional attributes from Syuzhet package using various of Azure components. - Data The Twitter dataset was collected daily using ASA job with Event Hub as the source and Blob storage as the sink. The collected dataset was stored in a Blob Storage in CSV format and downloaded using Azure Storage Explorer. Various Twitter handles were used for daily collecting sentiments for each day over a period of 1 week. ASA jobs were run daily for over a period of 1 hour to collect the info. For example, for Monday, handles such as (Monday, MondayMorning, mondaythoughts, MondayMood, MondayMotivation) were collected for analysis. - Retrieve Data Over of 25k collected and the following is the Azure Streaming Analytics Query Language (ASAQL) used to for the Twitter Stream *SELECT [CreatedAt[, [Topic], [SentimentScore], [Author], [Text] FROM EventHubInputName* Additional features were added to facilitate easier selection of info for R-scripts - Prepare Data No preparation of the info was done in Azure Machine Learning (AML) - Preprocess Data None of the AML modules pertaining to feature selection or preprocessing were employed inside the Azure Machine Learning - Algorithm In an effort to study various sentiments expressed, the following R-packages were employed *SentimentAnalysis: uses analyzeSentiment Syuzhet: uses get_nrc_sentiment emotion lexicon tm: various functions were utilized to remove stopwords, url. Whitespace and as well as restrict to particular encoding for this analysis.* - Results Two Execute R-Script modules were utilized to calculate and plot using the above packages. Each of the plots compares the sentiments across each day