I have a dataset of responses where people were requested to answer a set of questions. There's only one column of text data to process.
My challenge is; only very few respondents have actually written long texts that I found easy to process and gain insights from it. Most of the other responses are often found to be very short such as "Somewhat", "Yes", "No", "Larger extent". That too, it has been not possible to scale it ordinally because there's no logical order to it.
I have been able to use the longer text responses to gain insights on Sentiments, extract keywords and phrases and apply Machine learning such as RAKE and PMI. I used UDPIPE library with R.
However, for shorter "few words" responses, I am finding it really difficult to gain insights from these.
Is there any other machine learning technique possible with the current issue I'm having ? Or do I need to try any NLP technique ?
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I don't know if this is the right place to ask this but, i am trying to build a bot in Python that will read incoming messages on a Slack channel where customer post their issues such as 'unable to connect to VPN', 'can someone reply to my ticket' etc…
The bot will analyze the message, determine if the customer is angry or not, and then propose a solution until an agent is free to actually check the issue.
Now, I was experimenting with TextBlob for the sentiment analysis part, but I don't know which technologies to actually use to determine the issue based on specific keywords and provide a solution to the user. Can someone propose me some python libraries/technologies that I could use to achieve this ?
To be honest your question is to generic to answer in one go.
Nontheless, you first have to clearly define the scope of your project. In doing so, you might want to first do a quick literaty review (Google Scholar) to familiarize with the state of the art technologies and methods.
From my little experience, a common (maybe simple) technique (lexicon-based approach) used to determine the sentiment of a word, is to use a pre-compiled dictionary (you can create your own though) that contains words - sentiment mappings. For example:
word:tired, sentiment:negative, score:5
So each time the bot finds the keyword "tired" in a sentence it will assign its corresponding negative value (polarity) to the sentence.
You might want to consider applying POS tags in the input text, as sometimes nouns or ``verbs carry significant meaning, compared to adjectives for example.
Keep in mind though, that negative comments can be written in the form of sarcasm. Sarcasm detectioin is a more difficult task though.
Alternatively, you could try using a pre-trained model such as bert-base-multilingual-uncased-sentiment that can be found here in Hugging Face.
For more information on the matter you have a look at this post.
Again as I mentioned, you have to clearly define your goals. This will enable you to specify the libraries or methodology available to solve your problem. Hope my answer helps.
So, a little bit on my problem.
TL;DR
Can I use machine-learning instead of Elastic Search to find results depending on the user's text input? Is it a good idea?
I am working on a car spare parts project, and we have split the car into 300 parts that we store on the database, with some data for each part (weight, availability, etc).
When the customer inputs the text of his part, we need to be able to classify the part, and map it to one in our database.
The current way it's being done is by people on our team manually mapping the customer text with the parts on our database, we want to automate that process.
We tried using MongoDB text search, but it was often inaccurate since parts have different names in different parts of the country.
So we wanted something that got more accurate results, and improved by the more data we have, we immediately considered TensorFlow, after some research and taking part of Google's Machine Learning Crash Course, I got to that point where it specified:
Models can't learn from string values, so you'll have to perform some feature engineering to convert those values to something numeric
That would be useful in the case we have limited number of features as strings, but we don't know what the user will input as a text.
So, my questions are:
1- Can we use Machine Learning to map text input by the user with some documents on our database?
2- If we can do that, is it a good idea to favor it over other search tools like ElasticSearch?
3- Can ElasticSearch improve its results the more data we have? How?
4- How would you go about this problem?
Note: I'd be doing that in Node.js, and since TensorFlow.js is new, I am inclining to go for other solutions, but if push comes to shove, and the results are much better, I would definitely go there.
TL;DR: Yes and yes.
TS;WM:
This is a perfectly suited problem for machine learning. Especially so, if you have a database of past customer texts that have already been mapped to parts. Ideally, you have hundreds of texts mapped to each part. If that is present, you can design and train a network. And models can learn from string values with some engineering, and it's not that bad.
I'm not sure ElasticSearch would improve much on the network. I don't know much about auto parts trading, but as a wild guess, "the large round thingy that helps change direction" would never be mapped to "steering wheel" by ES but could be learned easily by a network - provided there are at least some examples of people using that text to specify steering wheel.
You can but don't have to necessarily use tensorflow.js for your network. The AI could run on your server as a webservice, and you'd just send over the customer's text to it and it would send back it's recommendations of part SKUs and names.
I'm embarking on a project for a non-profit organization to help process and classify 1000's of reports annually from their field workers / contractors the world over. I'm relatively new to NLP and as such wanted to seek the group's guidance on the approach to solve our problem.
I'll highlight the current process, and our challenges and would love your help on the best way to solve our problem.
Current process: Field officers submit reports from locally run projects in the form of best practices. These reports are then processed by a full-time team of curators who (i) ensure they adhere to a best-practice template and (ii) edit the documents to improve language/style/grammar.
Challenge: As the number of field workers increased the volume of reports being generated has grown and our editors are now becoming the bottle-neck.
Solution: We would like to automate the 1st step of our process i.e., checking the document for compliance to the organizational best practice template
Basically, we need to ensure every report has 3 components namely:
1. States its purpose: What topic / problem does this best practice address?
2. Identifies Audience: Who is this for?
3. Highlights Relevance: What can the reader do after reading it?
Here's an example of a good report submission.
"This document introduces techniques for successfully applying best practices across developing countries. This study is intended to help low-income farmers identify a set of best practices for pricing agricultural products in places where there is no price transparency. By implementing these processes, farmers will be able to get better prices for their produce and raise their household incomes."
As of now, our approach has been to use RegEx and check for keywords. i.e., to check for compliance we use the following logic:
1 To check "states purpose" = we do a regex to match 'purpose', 'intent'
2 To check "identifies audience" = we do a regex to match with 'identifies', 'is for'
3 To check "highlights relevance" = we do a regex to match with 'able to', 'allows', 'enables'
The current approach of RegEx seems very primitive and limited so I wanted to ask the community if there is a better way to solving this problem using something like NLTK, CoreNLP.
Thanks in advance.
Interesting problem, i believe its a thorough research problem! In natural language processing, there are few techniques that learn and extract template from text and then can use them as gold annotation to identify whether a document follows the template structure. Researchers used this kind of system for automatic question answering (extract templates from question and then answer them). But in your case its more difficult as you need to learn the structure from a report. In the light of Natural Language Processing, this is more hard to address your problem (no simple NLP task matches with your problem definition) and you may not need any fancy model (complex) to resolve your problem.
You can start by simple document matching and computing a similarity score. If you have large collection of positive examples (well formatted and specified reports), you can construct a dictionary based on tf-idf weights. Then you can check the presence of the dictionary tokens. You can also think of this problem as a binary classification problem. There are good machine learning classifiers such as svm, logistic regression which works good for text data. You can use python and scikit-learn to build programs quickly and they are pretty easy to use. For text pre-processing, you can use NLTK.
Since the reports will be generated by field workers and there are few questions that will be answered by the reports (you mentioned about 3 specific components), i guess simple keyword matching techniques will be a good start for your research. You can gradually move to different directions based on your observations.
This seems like a perfect scenario to apply some machine learning to your process.
First of all, the data annotation problem is covered. This is usually the most annoying problem. Thankfully, you can rely on the curators. The curators can mark the specific sentences that specify: audience, relevance, purpose.
Train some models to identify these types of clauses. If all the classifiers fire for a certain document, it means that the document is properly formatted.
If errors are encountered, make sure to retrain the models with the specific examples.
If you don't provide yourself hints about the format of the document this is an open problem.
What you can do thought, is ask people writing report to conform to some format for the document like having 3 parts each of which have a pre-defined title like so
1. Purpose
Explains the purpose of the document in several paragraph.
2. Topic / Problem
This address the foobar problem also known as lorem ipsum feeling text.
3. Take away
What can the reader do after reading it?
You parse this document from .doc format for instance and extract the three parts. Then you can go through spell checking, grammar and text complexity algorithm. And finally you can extract for instance Named Entities (cf. Named Entity Recognition) and low TF-IDF words.
I've been trying to do something very similar with clinical trials, where most of the data is again written in natural language.
If you do not care about past data, and have control over what the field officers write, maybe you can have them provide these 3 extra fields in their reports, and you would be done.
Otherwise; CoreNLP and OpenNLP, the libraries that I'm most familiar with, have some tools that can help you with part of the task. For example; if your Regex pattern matches a word that starts with the prefix "inten", the actual word could be "intention", "intended", "intent", "intentionally" etc., and you wouldn't necessarily know if the word is a verb, a noun, an adjective or an adverb. POS taggers and the parsers in these libraries would be able to tell you the type (POS) of the word and maybe you only care about the verbs that start with "inten", or more strictly, the verbs spoken by the 3rd person singular.
CoreNLP has another tool called OpenIE, which attempts to extract relations in a sentence. For example, given the following sentence
Born in a small town, she took the midnight train going anywhere
CoreNLP can extract the triple
she, took, midnight train
Combined with the POS tagger for example; you would also know that "she" is a personal pronoun and "took" is a past tense verb.
These libraries can accomplish many other tasks such as tokenization, sentence splitting, and named entity recognition and it would be up to you to combine all of these tools with your domain knowledge and creativity to come up with a solution that works for your case.
Help by editing my question title and tags is greatly appreciated!
Sometimes one participant in my corpus of "conversations" will refer to another participant using a nickname, usually an abbreviation or misspelling, but hereafter I'll just say "nicknames". Let's say I'm willing to manually tell my software whether or not I think various possible nicknames are in fact nicknames, but I want software to come up with a list of possible matches between the handle's that identify people, and the potential nicknames. How would I go about doing that?
Background on me and then my corpus: I have no experience doing natural language processing but I'm a competent data analyst with R. My data is produced by 70 teams, each forecasting the likelihood of 100 distinct events occurring some time in the future. The result that I have 70 x 100 = 7000 text files, containing the stream of forecasts participants make and the comments they include with their forecasts. I'll paste a very short snip of one of these text files below, this one had to do with whether the Malian government would enter talks with the MNLA:
02/12/2013 20:10: past_returns answered Yes: (50%)
I hadn't done a lot of research when I put in my previous
placeholder... I'm bumping up a lot due to DougL's forecast
02/12/2013 19:31: DougL answered Yes: (60%)
Weak President Traore wants talks if MNLA drops territorial claims.
Mali's military may not want talks. France wants talks. MNLA sugggests
it just needs autonomy. But in 7 weeks?
02/12/2013 10:59: past_returns answered No: (75%)
placeholder forecast...
http://www.irinnews.org/Report/97456/What-s-the-way-forward-for-Mali
My initial thoughts: Obviously I can start by providing the names I'm looking to match things up with... in the above example they would be past_returns and DougL (though there is no use of nicknames in the above). I wouldn't think it'd be that hard to get a computer to guess at minor misspellings (though I wouldn't personally know where to start). I can imagine that other tricks could be used, like assuming that a string is more likely to be a nickname if it is used much much more by one team, than by other teams. A nickname is more likely to refer to someone who spoke recently than someone who spoke long ago, or not at all on regarding this question. And they should be used in sentences in a manner similar to the way the full name/screenname is typically used in the corpus. But I'm interested to hear about simple approaches, as well as ones that try to consider more sophisticated techniques.
This could get about as complicated as you want to make it. From the semi-linguistic side of things, research topics would include Levenshtein Distance (for detecting minor misspellings of known names/nicknames) and Named Entity Recognition (for the task of detecting names/nicknames in the first place). Actually, NER's worth reading about, but existing systems might not help you much in your domain of forum handles and nicknames.
The first rough idea that comes to mind is that you could run a tokenized version of your corpus against an English dictionary (perhaps a dataset compiled from Wiktionary or something like WordNet) to find words that are candidates for names, then filter those through some heuristics (do they start with the same letters as known full names? Do they have a low Levenshtein distance from known names? Are they used more than once?).
You could also try some clustering or supervised ML algorithms against the non-word tokens. That might reveal some non-"word" tokens that often occur in the same threads as a given username; again, heuristics could help rule out some false positives.
Good luck; sounds like a fun problem - hope I mentioned at least one thing you hadn't already thought of.
I want to analyze answers to a web survey (Git User's Survey 2008 if one is interested). Some of the questions were free-form questions, like "How did you hear about Git?". With more than 3,000 replies analyzing those replies entirely by hand is out of the question (especially that there is quite a bit of free-form questions in this survey).
How can I group those replies (probably based on the key words used in response) into categories at least semi-automatically (i.e. program can ask for confirmation), and later how to tabularize (count number of entries in each category) those free-form replies (answers)? One answer can belong to more than one category, although for simplicity one can assume that categories are orthogonal / exclusive.
What I'd like to know is at least keyword to search for, or an algorithm (a method) to use. I would prefer solutions in Perl (or C).
Possible solution No 1. (partial): Bayesian categorization
(added 2009-05-21)
One solution I thought about would be to use something like algorithm (and mathematical method behind it) for Bayesian spam filtering, only instead of one or two categories ("spam" and "ham") there would be more; and categories itself would be created adaptively / interactively.
Text::Ngrams + Algorithm::Cluster
Generate some vector representation for each answer (e.g. word count) using Text::Ngrams.
Cluster the vectors using Algorithm::Cluster to determine the groupings and also the keywords which correspond to the groups.
You are not going to like this. But: If you do a survey and you include lots of free-form questions, you better be prepared to categorize them manually. If that is out of the question, why did you have those questions in the first place?
I've brute forced stuff like this in the past with quite large corpuses. Lingua::EN::Tagger, Lingua::Stem::En. Also the Net::Calais API is (unfortunately, as Thomposon Reuters are not exactly open source friendly) pretty useful for extracting named entities from text. Of course once you've cleaned up the raw data with this stuff, the actual data munging is up to you. I'd be inclined to suspect that frequency counts and a bit of mechanical turk cross-validation of the output would be sufficient for your needs.
Look for common words as keywords, but through away meaningless ones like "the", "a", etc. After that you get into natural language stuff that is beyond me.
It just dawned on me that the perfect solution for this is AAI (Artificial Artificial Intelligence). Use Amazon's Mechanical Turk. The Perl bindings are Net::Amazon::MechanicalTurk. At one penny per reply with a decent overlap (say three humans per reply) that would come to about $90 USD.