The role of Sentiment Analysis in Social Media Monitoring:

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I’ve posted tutorial/resources about the Technical Side of Sentiment Analysis on this Blog. Here are the Links, if you need them:

LingPipe (Java Based) | Python | R language resource | Microsoft’s Tool “Social Analytics

Apart from this, I’ve used other Tools per project requirements and It’s been fun designing and developing projects on “Sentiment Analysis” primarily using Social Media Monitoring. Having worked with clients on projects that use “Sentiment Analysis” – I reflected about the role of Sentiment Analysis in Social Media Monitoring. And in this blog post, I am sharing these reflections:

What is Social Media Monitoring?

Social Media Monitoring is a process of “monitoring” conversations happening on social media channels about your brand/company.

Is it NEW? Not really. The idea of monitoring or gathering data about what is being talked about the brand/company is not new. Earlier, it was newspapers and magazine-articles and now, it’s the social media channels including online news, forums and blogs and thus the name given to this process is “Social Media Monitoring”

brand monitoring social media

What is Sentiment Analysis?

Analyzing data to categorize it under a “sentiment” (emotion).

Example. Is this review saying positive, negative or neutral thing about our product.

sentiment analysis positive negative neutral

side-note: Sentiment analysis is often categorized under “Big Data Analytics”.

What’s the Role of Sentiment Analysis in Social Media Monitoring?

We’ve seen that in social media monitoring, we gather all online conversations about a brand/product/company. Now wouldn’t it be great to take the data that we have and bucket it under “Positive”, “Negative” or “Neutral” categories for further analysis?

So few questions that can be answered after we have results from sentiment analysis:

1) Are people happy or sad about our product?

2) What do they like about our product?

3) What do they hate about our service?

4) Is there a trend or seasonality in sentiment data?

Among other business insights that may be not be easily answerable with just plain text data.

Thus sentiment analysis is one of the step in social media monitoring that assists in analyzing sentiment of all the conversations happening on the social web about a brand/product.

That’s about this for this post. Here’s a related post: Three Data Collection Tips for Social Media Analytics

your comments are very welcome!

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Beginner’s Guide: Sentiment Analysis using Python on Windows

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This is beginner’s guide to sentiment analysis using Python NLTK on windows. We’ll start w/ installing Python and NLTK and then see how to perform sentiment analysis.

Step 1: Install Python & NLTK

I followed the steps listed on http://nltk.org/install.html

1. Search for python 2.7.3 for windows and install it.

2. Search for Python setup Tools for Windows and install it.

3. Install PIP (for win 64 bit), NLTK and PyYAML.

4. Test installation: Start>All Programs>Python27>IDLE, then type import nltk

Now,

5. Also type:

>>> Import random

6. And also install movie_reviews corpus by typing:

>>>nltk.download()

in the new window that opens, install the movie_reviews corpus.

python nltk download data

Step 2: Sentiment Analysis

I followed the code explained in the NLTK book in the section “document classification” in ch 6 learning to classify text. Here is the section: http://nltk.org/book/ch06.html#document-classification

Using the code I was able to run the Naive Bayes Classifier to categorize text:

python sentiment analysis

Conclusion:

In this post, we learned how to perform sentiment analysis using Python on windwos platform. NLTK supports classifiers other than Naive Bayes, and also there are resources that will help  you increase the accuracy of the classifier. And I hope that this post acts as a starting guide for you!

Related articles

Three Data Visualizations I liked this week:

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I have been working on creating Dashboards for one of my projects. As a part of the research, I looked at few Dashboards out their on the inter-webs. Here are three of them that I liked:

1. Social Media & Sentiment Analysis:

What I like about this Dashboard is the creative use of Data via Sentiment Analysis:

sentiment analysis social media dashboard

2. Microsoft Research’s Viral Search Project:

What a creative way to visualize viral content!

visualize viral social network data microsoft viral search

3. Social Media analytic’s Dashboard:

Nice one page social dashbaord!

social media analytics dashboard

Do you see the bottom right part of the report that shows you engagement levels by post type, if you want to compute it – here’s my blog post on that: Social Media Analytics. Facebook Page Smackdown: Status updates vs Images?

 

Recapping my social media activities during Jan 1 – Feb 24 2013:

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Recapping my social media activities during Jan 1 – Feb 20 2013:

That’s about it for this post.

If we want to read related past posts, here they are:

OCT 3 – OCT 10 2012

OCT 11 – OCT 18 2012

OCT 19 – NOV 11 2012

NOV 12 – DEC 31 2012

Let’s connect and converse on any of these people networks!

paras doshi blog on facebookparas doshi twitter paras doshi google plus paras doshi linkedin

Sentiment Analysis using LingPipe on windows 7:

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In this post, I’ll point you to the resource using which you can perform sentiment analysis using LingPipe on a windows OS. Along with that I’ll share couple of issues that I ran into when I was trying to run this demo on a Windows 7:

So first up, here’s the resource:

http://alias-i.com/lingpipe/demos/tutorial/sentiment/read-me.html

Now here are a couple of issues that I had:

1. Error: could not find or load the main class PolarityBasic

lingpipe could not find or load main class polaritybasic

To solve this error, you’ll need to build the files given under the C:\lingpipe-4.1.0\demos\tutorial\sentiment – we use ANT for this. Let’s see how to do that:

2. Building sentiment.jar using ant jar

After successfully downloading ant on windows and setting the ANT_HOME variable to c:\apache-ant-1.8.4 – I was still getting the error that ant is not a recognized command.

So I ran following commands:

C:\>set ANT_HOME=C:\apache-ant-1.8.1
C:\>set JAVA_HOME=C:\jdk1.6.0_24
C:\>set PATH=%ANT_HOME%\bin;%JAVA_HOME%\bin
C:\>ant -version
// it worked!

Thanks: http://stackoverflow.com/questions/5607664/installing-ant-ant-home-is-set-incorrectly-on-windows-7

Now I ran the following command:

build sentiment.jar ant lingpipe

3. In the tutorial they used POLARITY_DIR – I didn’t use that, Instead I just inputted c:\review_polarity because that’s where I unzipped the movie review dataset:

movie review sentiment analysis polarity

Here’s the screenshot about the command that does basic polarity analysis:

sentiment analysis lingpipe windows

And Thanks: http://stackoverflow.com/questions/15010184/lingpipe-and-sentiment-analysis/15011482

How to start Analyzing Twitter Data w/ R?

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Over the past few weeks, I have posted notes about Analyzing Twitter Data w/ R, listing them here:

1. Install R & RStudio

2. R code to download twitter data

3. Perform Sentiment Analysis on Twitter Data (in R)

Sentiment Analysis in R w/ Twitter data feeds

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I followed instructions on this site to perform sentiment analysis about Starbucks from Twitter data feeds.

Here are data visualizations:

1. Sentiment Analysis: Starbucks on Twitter

sentiment analysis starbucks on twitter

2. Comparison cloud:

comparison cloud data visualization

That’s about it for this post, Here are some related tutorials:

If you want to Install R on windows machine, here’s a Tutorial: http://parasdoshi.com/2012/11/13/lets-install-r-rstudio-on-windows-machine/

If you want to try out out Hadoop on windows, Hive and Hive excel add-in w/ Twitter Data, Here’s a Tutorial: http://parasdoshi.com/2012/11/16/how-to-load-twitter-data-into-hadoop-on-azure-cluster-and-then-analyze-it-via-hive-add-in-for-excel/

If you want to Grab Twitter search data using R and export to a tab delimited file. Here’s a tutorial: http://parasdoshi.com/2012/11/24/grab-twitter-search-data-using-r-and-export-to-a-tab-delimited-file/