sentiment analysis
Budget: $90 – $100 USD
Hi,
I want a sentiment analysis solution where the data point can be 10k-40k, either run in a PowerBI or a Python script/function which can be run in a Google Cloud to do the Following -
1. Analyze text in 35 different languages and determine if the text has any personal Informaiton like Name, email address, Home Address, City Name, Country Name, Phone Number, or any social security number. If there is personal info exists, it needs to flag it and remove the personal Informaiton and save it in another column
2. Analyze text in 35 different languages and determine if there is any negative info. If there is negative info exists, it needs to flag it and remove the personal Informaiton and save it in another column. Also, make a scoring based on how positive the Entire comment or text info's where 1 is the most positive and 0 is the lowest positive number.
3. Reading the text you need to create a tag, Example - If there is a question like - "How was your onboarding Experience". This should be tagged as "On-boarding".
If you use PowerBI Both translation and the positive-negative scaling come as a default capability. In that case, the only thing you really need to work on is Personal information detection and the tag.
If you want to use any other python library /Functions or any other python code that can do the same thing, it is completely fine with me.
I want a sentiment analysis solution where the data point can be 10k-40k, either run in a PowerBI or a Python script/function which can be run in a Google Cloud to do the Following -
1. Analyze text in 35 different languages and determine if the text has any personal Informaiton like Name, email address, Home Address, City Name, Country Name, Phone Number, or any social security number. If there is personal info exists, it needs to flag it and remove the personal Informaiton and save it in another column
2. Analyze text in 35 different languages and determine if there is any negative info. If there is negative info exists, it needs to flag it and remove the personal Informaiton and save it in another column. Also, make a scoring based on how positive the Entire comment or text info's where 1 is the most positive and 0 is the lowest positive number.
3. Reading the text you need to create a tag, Example - If there is a question like - "How was your onboarding Experience". This should be tagged as "On-boarding".
If you use PowerBI Both translation and the positive-negative scaling come as a default capability. In that case, the only thing you really need to work on is Personal information detection and the tag.
If you want to use any other python library /Functions or any other python code that can do the same thing, it is completely fine with me.