I am working regarding sentiment analysis of about 500 comments over a blog. The blog states the career frustrations of professionals in a particular profession (i.e it itself reveals the negative aspects related to that profession).
Firstly, I have performed the content analysis (using the QDA Miner) of the blog to find the career aspects that are causing career frustrations among the professions.
Next, I have planned for sentiment analysis (using Python NLTK Text Classification/ http://text-processing.com/demo/sentiment/ ) of the comments (given exclusively by the professionals associated with that particular career) on the blog post . However, on moving further I have obtained that in some comments, there is fragmentation of sentiments. The first fragment which is related to the blog gives a positive sentiment whereas, the second one which is related to descriptions of career aspects gives a negative polarity.
For eg: Excellent article (+ve Sentiment related to the blog post). the problem is that you learn this once you have spent 15 yrs in this field trying to achieve what you dreamt of...... it's a complete waste going into this career field in this country. (-ve Sentiment wrt career aspect) it's better if you aim early and move out...to the US or any other place of your liking...(-ve Sentiment wrt career aspect).
Kindly give me some advice on how to conduct sentiment analysis of such type of dual-natured comments that reflects sentiments for the blog post and the blog aspects itself.
Dear Sayoni,
To be honest I think you have chosen the long road to gain information about this topic.
You may easily obtain this information by asking direct questions in a questionnaire. Collecting data from blogs for the topic that participants would willingly share information with you in a questionnaire wouldn't be a good idea.
This is an interesting topic though. I hope you won't limit yourself in these blogs.
Good luck
I think the problem you are dealing with is "Sarcasm Detection", (as the example says). It's also a form of sentiment analysis, but slightly varying.
You can follow this paper for more info,
https://www.researchgate.net/publication/305324531_Sarcastic_Sentiment_Detection_in_Tweets_Streamed_in_Real_time_A_Big_Data_Approach
Article Sarcastic Sentiment Detection in Tweets Streamed in Real tim...
Please can you try using R to alleviate your difficulties. Try the link below
https://www.youtube.com/watch?v=2nXwNJO16OE
So u r doing directly sentiment analysis on these documents.
R u using any tool?
I have worked on Sentiment Analysis and the Easiest Tool for it is Rapidminer and it has many features which is necessary for Sentiment Anaysis.
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