Why does the sentiment analysis classified a negative text as positive?

AmosGH

I run some sentiment analysis but it gave me wrong analysis in the sense that a text that supposes to be negative was classified as positive. What accounts for this misinterpretation and what can be done to rectify it. Again how does the sentiment analysis from Aylien classified a text to be negative, positive and neutral?

lionelderkrikor

Hi @AmosGH

I don't know what are the algorithm(s) behind Aylien. Aylien is a 3th part extension, I think that, if you want more information about how this extension works, you will need to contact Aylien directly.
For your problem, have you tried other methods like : 

1. In RapidMiner : 
 - Extract Sentiment operator from the Toolbox extension (to install from the MarketPlace)
2. Python libraries : 
 - TextBlob
 - NLTK (Natural Language ToolKit)

Hope this helps,

Reagrds,

Lionel

Telcontar120

Also note that Aylien no longer supports their RapidMiner extension.  That won't answer the question as to why their text is misclassified, but it may mean you have a hard time getting help with it.  You should probably try one of the options that Lionel mentions or one of the other 3rd party text analysis extensions such as Rosette or MonkeyLearn.

tkaratasios

Can someone guide me with some problems I have about performing a sentiment analysis? Reading all the previews I see that there are several extentions about rapidminer. Having downloaded these extensions means that they work simultaneously or do I have to choose the prefered one? I see the same problem as the AmosGH
How do I perform this? In RapidMiner : 
 - Extract Sentiment operator from the Toolbox extension (to install from the MarketPlace)
Can these two be added in rapidminer? 
 - TextBlob
 - NLTK (Natural Language ToolKit)

Thank you for your time and forgive me for the number of questions I did

Pavithra_Rao

Hi @tkaratasios,

Here's quick tutorial videos on how to use operators (as part of extensions) and build the workflow in RapidMiner.

https://academy.rapidminer.com/learning-paths/get-started-with-rapidminer-and-machine-learning

Also, each operator comes with sample tutorial processess to learn more about how to use a specific operator.



Hope this helps!

Cheers,
Pavithra

tkaratasios

Thank you Pavithra for the provided help. I will see your recomendations but I also have another question which need some help
When I conduct a sentiment analysis using a phrase which would be estimated as positive instead of that rapidminer characterized it as negative ?

kayman

Classic sentiment analysis can be tricky as it (oversimplified) sums up negative and positive indicators and looks at the one who gets the highest count.

So it will not recognize irony, sarcasm, tongue in cheek and all the other ways we humans are able to use nice words to make something sound bad or the other way around. 

Apart from that the training data is very important, most if not all providers from commercial solutions trained their models on public and relatively generic data. So if your content does not match this well (because for instance you have a rather specific domain) your 'obvious giveaways' may not have been in the original data set and are therefore ignored for yours.

So this leaves you with the option to train your own, and if you have enough data to train you can get pretty good results (but never perfect, blame our language flexibility for that...) 

I personally like the Vader fork for NLTK, gave me good results and was rather easy to implement

tkaratasios

Thank you for the support Kayman. Please give some details about your suggestion. Refering to Vader means that it can be applied in rapidminer? Moreover how can I do some training to the program?