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"
Related
I have some texts in different languages and, potentially, with some typo or other mistake, and I want to retrieve their own vocabulary. I'm not experienced with NLP in general, so maybe I use some word improperly.
With vocabulary I mean a collection of words of a single language in which every word is unique and the inflections for gender, number, or tense are not considered (e.g. think, thinks and thought are are all consider think).
This is the master problem, so let's reduce it to the vocabulary retrieving of one language, English for example, and without mistakes.
I think there are (at least) three different approaches and maybe the solution consists of a combination of them:
search in a database of words stored in relation with each others. So, I could search for thought (considering the verb) and read the associated information that thought is an inflection of think
compute the "base form" (a word without inflections) of a word by processing the inflected form. Maybe it can be done with stemming?
use a service by any API. Yes, I accept also this approach, but I'd prefer to do it locally
For a first approximation, it's not necessary that the algorithm distinguishes between nouns and verbs. For instance, if in the text there were the word thought like both noun and verb, it could be considered already present in the vocabulary at the second match.
We have reduced the problem to retrieve a vocabulary of an English text without mistakes, and without consider the tag of the words.
Any ideas about how to do that? Or just some tips?
Of course, if you have suggestions about this problem also with the others constraints (mistakes and multi-language, not only Indo-European languages), they would be much appreciated.
You need lemmatization - it's similar to your 2nd item, but not exactly (difference).
Try nltk lemmatizer for Python or Standford NLP/Clear NLP for Java. Actually nltk uses WordNet, so it is really combination of 1st and 2nd approaches.
In order to cope with mistakes use spelling correction before lemmatization. Take a look at related questions or Google for appropriate libs.
About part of speech tag - unfortunately, nltk doesn't consider POS tag (and context in general), so you should provide it with the tag that can be found by nltk pos tagging. Again, it is already discussed here (and related/linked questions). I'm not sure about Stanford NLP here - I guess it should consider context, but I was sure that NLTK does so. As I can see from this code snippet, Stanford doesn't use POS tags, while Clear NLP does.
About other languages - google for lemmatization models, since algorithm for most languages (at least from the same family) is almost the same, differences are in training data. Take a look here for example of German; it is a wrapper for several lemmatizers, as I can see.
However, you always can use stemmer at cost of precision, and stemmer is more easily available for different languages.
Topic Word has become an integral part of the rising debate in the present world. Some people perceive that Topic Word (Synonyms) beneficial, while opponents reject this notion by saying that it leads to numerous problems. From my point of view, Topic Word (Synonyms) has more positive impacts than negative around the globe. This essay will further elaborate on both positive and negative effects of this trend and thus will lead to a plausible conclusion.
On the one hand, there is a myriad of arguments in favour of my belief. The topic has a plethora of merits. The most prominent one is that the Topic Word (Synonyms). According to the research conducted by Western Sydney University, more than 70 percentages of the users were in favour of the benefits provided by the Topic Word (Synonyms). Secondly, Advantage of Essay topic. Thus, it can say that Topic Word (Synonyms) plays a vital role in our lives.
On the flip side, critics may point out that one of the most significant disadvantages of the Topic Word (Synonyms) is that due to Demerits relates to the topic. For instance, a survey conducted in the United States reveals that demerit. Consequently, this example explicit shows that it has various negative impacts on our existence.
As a result, after inspection upon further paragraphs, I profoundly believe that its benefits hold more water instead of drawbacks. Topic Word (Synonyms) has become a crucial part of our life. Therefore, efficient use of Topic Word (Synonyms) method should promote; however, excessive and misuse should condemn.
I would like to compare how complex (varied or predictable) are my three corpora. They are from different topics, so some vocabulary is different, some is the same. Looking at one of the data sets it's clear that the syntax is more difficult than in the other two, sentences are longer, etc. I built word N-Gram language models using the SRILM toolkit (I'm new to language modelling) with the idea that I can then compare these models. One measure mentioned in relation to language models is perplexity. I'm confused about the following question: Can I just use perplexities of the three LMs directly as a measure of how varied are the corpora? The vocabulary and the sizes of the corpora are different, so now I think that this won't be a good comparison. I also built LMs from POS-Tags but the quality of the POS-Tagging result is not good because the language is from fora, has spelling mistakes, ungrammatical sentences and so on. What measures could be used to compare complexity of corpora from different domains? I'd appreciate your advise.
[I'm new to Stackexchange. I posted this on Crossvalidated, but I think maybe here is more appropriate forum.]
"I also built LMs from POS-Tags but the quality of the POS-Tagging result is not good because the language is from fora, has spelling mistakes, ungrammatical sentences and so on."
Aside from it being noisy, like you pointed out, you should think carefully about whether particular linguistic features are useful in your analysis. Does one corpus having proportionally more nouns move you toward what you want to learn about the corpora? Maybe in something like authorship attribution, but I can't really think of anywhere else that's effective.
If data sparsity is an issue, LSI can help by collapsing related terms together. This could also help with the spelling issues, collapsing poorly spelt words with their correct counterparts if they appear in similar contexts.
"The vocabulary and the sizes of the corpora are different, so now I think that this won't be a good comparison."
It's not the end of the world. Having more data is always better, but you can work with what you have.
If you haven't chosen a language model yet, there's a few decisions you have to make:
Are you going to smooth the data? How?
Are you going to use an advanced technique to better exploit the data, such as Latent Semantic Indexing (LSI)?
You mention that you have a language model; I'm assuming your language model is a probability distribution such that P(N-gram|topic). If this is correct, you've already normalized the data, so the two probability distributions should be readily comparable. Having more data would get you a more reliable result, but if your corpora are "big enough" to sample each topic reliably, you can move right into comparison.
As for comparison, try the KL-Divergence. KL-Divergence is "a measure of the information lost when Q is used to approximate P." Less loss means that the corpora are more similar. If you want a symmetric comparison, a "cheap" way to do it is to add D(P||Q) + D(Q||P). Note, though:
The KL divergence is only defined if Q(i)=0 ⇒ P(i)=0, for all i (absolute continuity).
So you'll have to smooth, in some manner.
Are there any known ways (above and beyond statistical analysis, but not necessarily excluding it as being part of the solution) to relate sentences or concepts to one another using Natural Language Processing. Thus far I've only worked with NLTK and Stanford-NLP to aid in my project, but I am open to alternative open source solutions.
As an example take the following George Orwell essay (http://orwell.ru/library/essays/wiw/english/e_wiw). Suppose I gave the application the sentence
"What are George Orwell's opinions on writers."
or perhaps
"George Orwell believes writers enjoy writing to express their creativity, to make a point and for their egos."
Might yield lines from the essay like
"The aesthetic motive is very feeble in a lot of writers, but even a pamphleteer or writer of textbooks will have pet words and phrases which appeal to him for non-utilitarian reasons; or he may feel strongly about typography, width of margins, etc."
or
"Serious writers, I should say, are on the whole more vain and self-centered than journalists, though less interested in money."
I understand that this is not easy and I may not achieve much accuracy, but I was hoping for ideas on what already exists and what I could try to start off, or at least get the best results possible based on what is already known and out there.
The simplest way of doing this might be using some distance functions (such as Cosine similarity) between your query sentence and the sentence pool. It's easy to implement. Create a vocabulary from the text collection and each sentence is represented as a vector. You can use TF-IDF to represent values in the vector, and calculate the cosine similarity between sentences, and get the highest scored sentence with respect to your query sentence.
Or you can build index from your corpus and use for example Lucene and let it do the work for you.
You may also consider using LSA (Latent Semantic Analysis) where you can get the similarity between sentences.
From what I understand from your question (and also your comment) is you are more interested in understanding the meaning of individual sentence and then equate with each other in proximity. Statistical approach, in my opinion, is more for "getting a feel" of the sentence than understanding it. In my opinion I would suggest deep parsing approach.
Deep parse the sentence, understand what roles the words play in the sentence, understand what the subject-verb-object model (left to right parsing and such techniques) and then have a vocabulary that helps you categorise the nouns and verbs.
e.g.
"Serious writers, I should say, are on the whole more vain and self-centered than journalists, though less interested in money."
Parsing this sentence, lets you understand the subject of the sentence is "serious writers" (serious being an adjective, writers basically). In the verb form it states "are" (current state) and "interested". Each verb then points to some more vocabulary including adjectives. If you arrange this vocabulary in correct manner (and keep building it) I think you should get somewhere with your problem.
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
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).