SlangSD: Building and Using a Sentiment Dictionary of Slang Words for Short-Text Sentiment Classification. ∙ by Liang Wu, et al. Sentiment in social media is increasingly considered as an important resource for customer segmentation, market understanding, and tackling other socio-economic issues. Request SentiStrength. Please complete the information below to receive a link to download SentiStrength and its data files. It is a Windows program but works with.

The following is a tutorial for conducting a quality sentiment analysis of social media data (in this case Twitter). Download torrent planet nomads v0.9.2.2.23362 for mac free. I describe what sentiment analysis is, how it started, and why it is important.

I also offer a sentiment analysis process that I believe sums up the technique. I then introduce a valuable tool called SentiStrength. Following data cleaning and analysis, sentiment is visualized.As of now there isn’t a comprehensive (or even a brief tutorial) for this tool. So the motivation to write this tutorial stems from this shortage. SentiStrength has already been employed by researchers and findings have been published in a range of scholarly research journals.

I am quite confident that you will find this sentiment analysis tutorial beneficial.What is Sentiment Analysis?Sentiment analysis is the automated process of understanding opinions and emotions about a given subject from written or spoken language. Sentiment analysis is also known as opinion mining, opinion extraction, sentiment mining, subjectivity analysis, affect analysis, emotion analysis, and review mining.According to the Merriam-Webster’s Collegiate Dictionary, sentiment is defined as an attitude, thought, or judgment prompted by feeling.Sentiment analysis presents an active area of research in natural language processing (NLP). NLP is considered a sub-field in artificial intelligence whereby computers are able to interpret and process human language.How it all started?Sentiment analysis has been used across various disciplines. It is believed to have started from computer science. Later, management and then social sciences adopted sentiment analysis. Sentiment analysis has been extensively used in linguistic and machine learning studies.Large corporations have built their own in-house capabilities (e.g., Microsoft, Google, IBM, SAP, and SAS).Basic Sentiment Analysis: Classifying the polarity of a given text at the document, sentence, or tweet—positive, negative, or neutral.Advanced Sentiment Analysis: Understanding emotional states. For example, happy, angry, and sad.Why is it important?Sentiment analysis has attracted interest from researchers, journalists, companies, and governments.

Opinions and sentiments are extracted to create structured and actionable knowledge that can be used by a decision maker.The advent of social media has increased the value of sentiment analysis. Social networks are not only fueling the digital revolution, but also enabling the expression and spread of emotions and opinions through the network.Leveraging of new media requires constant monitoring of information.

In the political arena, sentiments can determine election outcomes; business carefully guard their brand image and user sentiment on social media needs to be constantly monitored.Issues in Sentiment AnalysisThe most problematic figures of speech in NLP are irony and sarcasm. Another issue is of the rules to detect implicit sentiment (e.g., through misspellings or exclamation marks).A sentiment analysis program typically achieves 70% accuracy in classifying sentiment.Human raters typically only agree about 80% (Ogneva, 2012)Sentiment Analysis Process. Topic IdentificationWhat are you interested in knowing? State the research question. Why does it matter? Who cares?. Medium IdentificationIdentify where you want to study the sentiment.

Will it be user generated content on social media? (YouTube comments, tweets on Twitter, Facebook posts, blogposts etc.). Content SearchDefine keywords through which you will get the desired data. Clearly defined search parameters are of vital importance in getting the right kind of data that relates to the initial research questions. Data CleaningRaw data is full of noise.

Data cleaning (especially social media data) requires ample sifting. Spam, fake accounts, data produced by bots, different languages etc. You will notice that the data file has been cleaned for (i) Retweets, (ii) languages other than English (iii) text that made no sense.For example, Spanish language tweets were deleted using the “filter” function in Excel.We will export this worksheet and save it as a.txt file.Following is a screenshot of the datafile in.TXT format.The clean data file just containing the tweets is ready to be analyzed for sentiment in SentiStrength. Sentiment Analysis with SentiStrength 1. Download SentiStrengthDownload program and zip file SentiStrengthData.zip fromFill in the fields above with your name, email, and organization.

You will be prompted to save the zip file on your computer. Save it in a new folder on your computer.Unzip SentiStrengthData.zip, then start SentiStrength.exe and point to the unzipped SentiStrengthData folder.Click on the.exe file and launch SentiStrength. As you will notice, the most recent version is 2.3Explore the top menus. “Sentiment Strength Analysis” gives a list of options regarding the type of analysis that can be done.For this tutorial, we will be selecting “Analyse All Texts in File (each line separately)”.

This is because our data file in.txt format contains all tweets in separate lines.The following screenshot depicts the “Sentiment Analysis Options” which allows you to choose how you want your analysis to be done.We can leave the default options selected.From the “Sentiment Strength Analysis” menu, we will be selecting “Analyse All Texts in File (each line separately)”. You will be prompted to choose the data file. Select the clean data file in.txt format.SentiStrength will now analyze the data and prompt to save a data file in which the sentiment has been performed (the file name will have “+results”).This new file is in.txt format and now has to be imported in Excel so that the analysis can be understood.Excel has a text import wizard which works when you try to open a.txt file.Once the three steps are followed (as shown above), the data file with the results for the sentiment become visible in Excel in rows and columns.As you can see above, there is a sentiment column for negative and positive. There is also a column for emotion rationale which provides the sentiment score next to each word in the tweet.The final step is to visualize the overall sentiment by creating a new worksheet with the two sentiment columns.

While selecting the sentiment columns, click on “Insert” and then select a “Column” chart to create a chart.You can create even better visualizations using Excel. As you can see in the above depiction, a simple column chart gives a general idea about the overall sentiment from this dataset.

Just providing some preamble for robg's brief description of installing:Go to SourceForge's smartmontools pageUnder 'Latest File Releases' is smartmontools 5.36 (Apr 12, 2006). Since a few years, there is a bad sector on one of my hard drives.Disk Utility does not report anything, but smartctl saysError 419 occurred at disk power-on lifetime: 13095 hours (545 days + 15 hours) When the command that caused the error occurred, the device was active or idle. You can get your serial number from the drive. This is very handy to create an inventory of your disks especially if you have multiple identical drives.-m. Smartmontools for mac. Check the docs for more info.3.