I have a list of several dozen product attributes that people are concerned with, like
Financing
Manufacturing quality
Durability
Sales experience
and several million free-text statements from customers about the product, e.g.
"The financing was easy but the housing is flimsy."
I would like to score each free text statement in terms of how strongly it relates to each of the attributes, and whether that is a positive or negative association.
In the given example, there would be a strong positive association to Financing and a strong negative association to Manufacturing quality.
It feels like this type of problem is probably the realm of Natural Language Programming (NLP). However, I spent several hours reading up on things like OpenNLP and NLTK and find there's so much domain specific terminology that I cannot figure out where to focus to solve this specific problem.
So my three-part question:
Is NLP the correct route to solve this class of problem?
What aspect of NLP should I focus on learning for this specific problem?
Are there alternatives I have not considered?
A resource you might find handy is SentiWordNet. (http://sentiwordnet.isti.cnr.it/) Which is like a dictionary that has a sentiment grade for words. It will tell you to what degree it thinks a word is positive, negative, or objective.
You can then combine that with some nltk code that looks through your sentences for the words you want to associate the sentiment with. So you would write a script to get some level of meaningful chunks of text that surround the words you were looking at, maybe sentence or clause level. Then you can have another thing that runs through the surrounding words and grab all the sentiment scores from the SentiWordNet.
I have some old code that did this and can place on github if you'd like, but you'd still need to make your own request for SentiWordNet.
I guess your problem is more on association rather than just classification. Now moving forward with this assumption:
Is NLP the correct route to solve this class of problem?
Yes.
What aspect of NLP should I focus on learning for this specific problem?
Part of speech tagging
Sentiment analysis
Maximum entrophy
Are there alternatives I have not considered?
In depth study of automata theory with respect to NLP will help you a lot, it helped me a lot in grasping the implementations like OpenNLP.
Yes, this is a NLP problem by the name of Sentiment analysis. Sentiment analysis is an active research area with different approaches and a task where a lot of other NLP-methods have to work together, so it is certainly not the easiest field to get started with in NLP.
A more or less recent survey of the academic research in the field can be found in Pang & Lee (2008).
Related
I've been working on a small, personal project which takes a user's job skills and suggests the most ideal career for them based on those skills. I use a database of job listings to achieve this. At the moment, the code works as follows:
1) Process the text of each job listing to extract skills that are mentioned in the listing
2) For each career (e.g. "Data Analyst"), combine the processed text of the job listings for that career into one document
3) Calculate the TF-IDF of each skill within the career documents
After this, I'm not sure which method I should use to rank careers based on a list of a user's skills. The most popular method that I've seen would be to treat the user's skills as a document as well, then to calculate the TF-IDF for the skill document, and use something like cosine similarity to calculate the similarity between the skill document and each career document.
This doesn't seem like the ideal solution to me, since cosine similarity is best used when comparing two documents of the same format. For that matter, TF-IDF doesn't seem like the appropriate metric to apply to the user's skill list at all. For instance, if a user adds additional skills to their list, the TF for each skill will drop. In reality, I don't care what the frequency of the skills are in the user's skills list -- I just care that they have those skills (and maybe how well they know those skills).
It seems like a better metric would be to do the following:
1) For each skill that the user has, calculate the TF-IDF of that skill in the career documents
2) For each career, sum the TF-IDF results for all of the user's skill
3) Rank career based on the above sum
Am I thinking along the right lines here? If so, are there any algorithms that work along these lines, but are more sophisticated than a simple sum? Thanks for the help!
The second approach you explained will work. But there are better ways to solve this kind of problem.
At first you should know a little bit about language models and leave the vector space model.
In the second step based on your kind of problem that is similar to expert finding/profiling you should learn a baseline language model framework to implement a solution.
You can implement A language modeling framework for expert finding with a little changes so that the formulas can be adapted to your problem.
Also reading On the assessment of expertise profiles will give you a better understanding of expert profiling with the framework above.
you can find some good ideas, resources and projects on expert finding/profiling at Balog's blog.
I would take SSRM [1] approach to expand query (job documents) using WordNet (extracted database [2]) as semantic lexicon - so you are not constrained only to direct word-vs-word matches. SSRM has its own similarity measure (I believe the paper is open-access, if not, check this: http://blog.veles.rs/document-similarity-computation-models-literature-review/, there are many similarity computation models listed). Alternativly, and if your corpus is big enough, you might try LSA/LSI[3,4] (also covered on the page) - without using external lexicon. But, if it is on English, WordNet's semantic graph is really rich in all directions (hyponims, synonims, hypernims... concepts/SinSet).
The bottom line: I would avoid simple SVM/TF-IDF for such concrete domain. I measured really serious margin of SSRM, over TF-IDF/VSM (measured as macro-average F1, 5-class single label classification, narrow domain).
[1] A. Hliaoutakis, G. Varelas, E. Voutsakis, E.G.M. Petrakis, E. Milios, Information Retrieval by Semantic Similarity, Int. J. Semant. Web Inf. Syst. 2 (2006) 55–73. doi:10.4018/jswis.2006070104.
[2] J.E. Petralba, An extracted database content from WordNet for Natural Language Processing and Word Games, in: 2014 Int. Conf. Asian Lang. Process., 2014: pp. 199–202. doi:10.1109/IALP.2014.6973502.
[3] P.W. Foltz, Latent semantic analysis for text-based research, Behav. Res. Methods, Instruments, Comput. 28 (1996) 197–202. doi:10.3758/BF03204765.
[4] A. Kashyap, L. Han, R. Yus, J. Sleeman, T. Satyapanich, S. Gandhi, T. Finin, Robust semantic text similarity using LSA, machine learning, and linguistic resources, Springer Netherlands, 2016. doi:10.1007/s10579-015-9319-2.
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.
I've been working on document level sentiment analysis since past 1 year. Document level sentiment analysis provides the sentiment of the complete document. For example - The text "Nokia is good but vodafone sucks big time" would have a negative polarity associated with it as it would be agnostic to the entities Nokia and Vodafone. How would it be possible to get entity level sentiment, like positive for Nokia but negative for Vodafone ? Are there any research papers providing a solution to such problems ?
You can try Aspect-level or Entity-level Sentiment Analysis. There are good efforts have been already done to find the opinions about the aspects in a sentence. You can find some of works here. You can also go further and deeper and review those papers that are related to feature (aspect) extraction. What does it mean? Let me give you an example:
"The quality of screen is great, however, the battery life is short."
Document-level sentiment analysis may not give us the real sense of this document here because we have one positive and one negative sentence in the document. However, by aspect-based (aspect-level) opinion mining, we can figure out the senses/polarities towards different entities in the document separately. By doing feature extraction, in the first step, you try to find the features (aspects) in different sentences (in here "quality of screen" or simply "quality" and "battery life"). Afterwards, when you have these aspects, you try to extract opinions related to these aspects ("great" for "quality" and "short" for "battery life"). In researches and academic papers, we also name features (aspects) as target words (those words or entities on which users comment), and the opinions as opinion words, the comments that have been stated about the target words.
By searching the keywords that I have just mentioned, you can become more familiar with these concepts.
You could look for entities and their coreferents, and have a simple heuristic like giving each entity sentiment from the closest sentiment term, perhaps closest by distance in a dependency parse tree, as opposed to linearly. Each of those steps seems to be an open research topic.
http://scholar.google.com/scholar?q=entity+identification
http://scholar.google.com/scholar?q=coreference+resolution
http://scholar.google.com/scholar?q=sentiment+phrase
http://scholar.google.com/scholar?q=dependency+parsing
This can be achieved using Google Cloud Natural Language API.
I also tried getting research articles on this but haven't found any. I would suggest you to try using the aspect based sentiment analysis algorithms. The similarity i found is there we recognize aspects of a single entity in a sentence and then find the sentiment of each aspect.Similarly we can train our model using the same algorithm which can detect the entities as it does for aspects and find the sentiment of such entities. I didn't try this but I am going to.Let me know if this worked or not.Also there are various ways to do this. The following are the links for few articles.
http://arxiv.org/pdf/1605.08900v1.pdf
https://cs224d.stanford.edu/reports/MarxElliot.pdf
Let's say I have a bunch of essays (thousands) that I want to tag, categorize, etc. Ideally, I'd like to train something by manually categorizing/tagging a few hundred, and then let the thing loose.
What resources (books, blogs, languages) would you recommend for undertaking such a task? Part of me thinks this would be a good fit for a Bayesian Classifier or even Latent Semantic Analysis, but I'm not really familiar with either other than what I've found from a few ruby gems.
Can something like this be solved by a bayesian classifier? Should I be looking more at semantic analysis/natural language processing? Or, should I just be looking for keyword density and mapping from there?
Any suggestions are appreciated (I don't mind picking up a few books, if that's what's needed)!
Wow, that's a pretty huge topic you are venturing into :)
There is definitely a lot of books and articles you can read about it but I will try to provide a short introduction. I am not a big expert but I worked on some of this stuff.
First you need to decide whether you are want to classify essays into predefined topics/categories (classification problem) or you want the algorithm to decide on different groups on its own (clustering problem). From your description it appears you are interested in classification.
Now, when doing classification, you first need to create enough training data. You need to have a number of essays that are separated into different groups. For example 5 physics essays, 5 chemistry essays, 5 programming essays and so on. Generally you want as much training data as possible but how much is enough depends on specific algorithms. You also need verification data, which is basically similar to training data but completely separate. This data will be used to judge quality (or performance in math-speak) of your algorithm.
Finally, the algorithms themselves. The two I am familiar with are Bayes-based and TF-IDF based. For Bayes, I am currently developing something similar for myself in ruby, and I've documented my experiences in my blog. If you are interested, just read this - http://arubyguy.com/2011/03/03/bayes-classification-update/ and if you have any follow up questions I will try to answer.
The TF-IDF is a short for TermFrequence - InverseDocumentFrequency. Basically the idea is for any given document to find a number of documents in training set that are most similar to it, and then figure out it's category based on that. For example if document D is similar to T1 which is physics and T2 which is physics and T3 which is chemistry, you guess that D is most likely about physics and a little chemistry.
The way it's done is you apply the most importance to rare words and no importance to common words. For instance 'nuclei' is rare physics word, but 'work' is very common non-interesting word. (That's why it's called inverse term frequency). If you can work with Java, there is a very very good Lucene library which provides most of this stuff out of the box. Look for API for 'similar documents' and look into how it is implemented. Or just google for 'TF-IDF' if you want to implement your own
I've done something similar in the past (though it was for short news articles) using some vector-cluster algorithm. I don't remember it right now, it was what Google used in its infancy.
Using their paper I was able to have a prototype running in PHP in one or two days, then I ported it to Java for speed purposes.
http://en.wikipedia.org/wiki/Vector_space_model
http://www.la2600.org/talks/files/20040102/Vector_Space_Search_Engine_Theory.pdf
Opinion Mining/Sentiment Analysis is a somewhat recent subtask of Natural Language processing.Some compare it to text classification,some take a more deep stance towards it. What do you think about the most challenging issues in Sentiment Analysis(opinion mining)? Can you name a few?
The key challenges for sentiment analysis are:-
1) Named Entity Recognition - What is the person actually talking about, e.g. is 300 Spartans a group of Greeks or a movie?
2) Anaphora Resolution - the problem of resolving what a pronoun, or a noun phrase refers to. "We watched the movie and went to dinner; it was awful." What does "It" refer to?
3) Parsing - What is the subject and object of the sentence, which one does the verb and/or adjective actually refer to?
4) Sarcasm - If you don't know the author you have no idea whether 'bad' means bad or good.
5) Twitter - abbreviations, lack of capitals, poor spelling, poor punctuation, poor grammar, ...
I agree with Hightechrider that those are areas where Sentiment Analysis accuracy can see improvement. I would also add that sentiment analysis tends to be done on closed-domain text for the most part. Attempts to do it on open domain text usually winds up having very bad accuracy/F1 measure/what have you or else it is pseudo-open-domain because it only looks at certain grammatical constructions. So I would say topic-sensitive sentiment analysis that can identify context and make decisions based on that is an exciting area for research (and industry products).
I'd also expand his 5th point from Twitter to other social media sites (e.g. Facebook, Youtube), where short, ungrammatical utterances are commonplace.
I think the answer is the language complexity, mistakes in grammar, and spelling. There is vast of ways people expresses there opinions, e.g., sarcasms could be wrongly interpreted as extremely positive sentiment.
The question may be too generic, because there are several types of sentiment analysis (document level, sentence level, comparative sentiment analysis, etc.) and each type has some specific problems.
Generally speaking, I agree with the answer by #Ian Mercer, and I would add 3 other issues:
How to detect a more in depth sentiment/emotion. Positive and negative is a very simple analysis, one of the challenge is how to extract emotions like how much hate there is inside the opinion, how much happiness, how much sadness, etc.
How to detect the object that the opinion is positive for and the object that the opinion is negative for. For example, if you say "She won him!", this means a positive sentiment for her and a negative sentiment for him, at the same time.
How to analyze very subjective sentences or paragraphs. Sometimes even for humans it is very hard to agree on the sentiment of this high subjective texts. Imagine for a computer...
Although this is a little bit an old question, let me add some note related to Arabic sentiment anlsysis in specific. Arabic language has morphological complexities and dialectal varieties which require advanced preprocessing and lexical building processes that surpass what is needed for the English language.
Please, refer to
"https://www.researchgate.net/publication/280042139_Survey_on_Arabic_Sentiment_Analysis_in_Twitter"
"https://link.springer.com/chapter/10.1007/978-3-642-35326-0_14"