Dr. Kushin | Department of Communication | Shepherd University
Project #2: Sentiment Analysis
Comm 435: Communication Rese...
Dr. Kushin | Department of Communication | Shepherd University
It is important that you understand how AFINN scores positi...
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Sentiment Analysis of Twitter Posts: Assignment

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This is a project assignment I use in my Communication Research class. Students use Yoshikoder, a free computer-assisted content analysis software, to learn about what sentiment analysis is and the "guts" of how they are conducted.

You can see learn more about the class and the project on my Scdribd account where I share the syllabus and other activities, as well as on my blog: http://mattkushin.com/2014/04/01/applied-research-class-sentiment-analysis-project-reflection/

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Sentiment Analysis of Twitter Posts: Assignment

  1. 1. Dr. Kushin | Department of Communication | Shepherd University Project #2: Sentiment Analysis Comm 435: Communication Research Summary Due Date See syllabus Percent of total grade 15% Purpose: To teach sentiment analysis of social media using quantitative computer-assisted content analysis Method: Quantitative Computer-assisted Content Analysis Notes:Group Assignment; We will do data analysis in class and jumpstart the writing of your methods and results (see syllabus); There is a Group Report Card after to evaluate your participation in the group. Things You Will Learn: Intermediate research report writing Using a problem statement, campaign objectives to create research objective and RQs/hypotheses How to do computer-assisted content analysis. How and why organizations do sentiment analysis, and their limitations. Writing Secondary Research and Methods sections Interpreting Data Using Computer-assisted content analysis software Deliverables: - Title Page - Problem Overview - Includes problem statement, campaign goals & objectives (PROVIDED BY ME, YOU ORGANIZE THEM AND ADD ANY NEEDED INFO), and research objective(s), and research question(s) (which you will write) - Secondary research literature review (about 2 double-space pages; 6 sources cited in text [may include the 3 listed below]) - Methods section (about 1 page double space) o Which should talk about where your methods came from, including the AFIN dictionary. - Results (length: as needed) - Discussion (length: as needed) - References – An alphabetic list of your references Info to Help you Write the Above Sections (note: there is a guide I will give in class to help you write methods, results, discussion. The below info will help you answer questions, and thus write your paper) Data: A sample of Tweets containing “NAME OF BRAND REDACTED” in them were collected over the course of several days during DATE. The date for the data collection was selected for convenience, due to a pressing desire from the firm to get the study done asap. Download data on Sakai (if it is not under ‘course documents’ on Sakai, email me!). A total of ENTER # HERE Tweets are in the data. Some sources you may want to cite in your secondary research: Your class text talks about Content Analysis and measuring sentiment! http://www.yoshikoder.org/index.html The original AFINN article: http://arxiv.org/pdf/1103.2903v1.pdf http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6010 Data Analysis: Sentiment For sentiment analysis, you will use an existing dictionary, which has been tested in prior research. It is called AFINN and has been designed for microblogs (e.g., Twitter). You will need to download the dictionary file and open it in Yoshikoder. (if it is not under ‘course documents’ on Sakai, email me!) Considerations for your Methods Section
  2. 2. Dr. Kushin | Department of Communication | Shepherd University It is important that you understand how AFINN scores positive & negative, using a +5 to -5 scale. You will have to decide how you want to operationalize your variables. Some possible operationalizations: Favorable / positive Sentiment - leaves the reader more likely to see organization favorably. Negative Sentiment – leaves reader more likely to see organization negatively. Neutral – no sentiment shown. The Situation Problem: HERE I DESCRIBE A SITUATION ABOUT A BRAND THAT IS WELL KNOWN ON THE SOCIAL WEB, PARTICULARLY ON TWITTER. SO BRANDS THAT ARE GOOD TO CHOOSE MAY INCLUDE BRANDS THAT USE SOCIAL MEDIA HEAVILY TO EGNAGE CUSTOMERS, CUSTOMER SERVICE, ETC. I CHOSE AN ONLINE RETAILER. I CHOOSE SOME SORT OF CIRCUMSTANCE THAT PRODUCED THE PROBLEM OR OPPORTUNITY – IN THIS CASE I MADE ONE UP BASED ON SOMEWHAT SIMILAR CIRCUMSTANCES THAT HAD HAPPENED TO THE BRAND I CHOSE. I THEN DESIGN MY GOALS, BENCHMARKS, AND KPI AROUND THAT. SO FEEL FREE TO MODIFY WHAT I HAVE BELOW. Campaign Goals and Objective: The campaign goal is to maintain BRAND’S great customer loyalty and satisfaction. The campaign objective is: 1) To maintain excellent perceptions of BRAND [outtake] with our valued customers as measured with 65% positive sentimenttoward BRANDon Twitter [desired outcome] You must determine: What is the research objective? What is the RQ or hypothesis? Benchmarks Two summers ago, BEFORE EVENT THAT HAPPENED IN MY PROBLEM STATEMENT ABOVE, THE BRAND had a 70% positive sentiment. This summer, after THE EVENT THAT HAPPENED IN MY PROBLEM STATEMENT AGOVE, research showed that sentiment had dropped to 50% positive. Key Performance Indicators 65% Positive sentiment on Twitter towards THE BRAND.

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