Need some info on machine learning, especially sentiment analysis. I need a software that can parse through a comment(collected from social media platforms) and then judge its polarity(positiveness vs negativeness) on multiple attributes.
Say - the attributes are cleanliness, service-promptness, ease of room booking
And the comment is -
" was able to book a room easily. However rooms werent very clean"
Then the result would be - 1. Cleanliness- negative 2. ease of booking - positive 3. promptness- neutral
Any leads on what software I can go for or if there are any pre-written programs on this in a language that is easily available online.
I'm researching the same area as you, from what i found out, SVM Classifier gives the best result regarding sentiment-analysis
Related
The question is about using a chat-bot framework in a research study, where one would like to measure the improvement of a rule-based decision process over time.
For example, we would like to understand how to improve the process of medical condition identification (and treatment) using the minimal set of guided questions and patient interaction.
Medical condition can be formulated into a work-flow rules by doctors; possible technical approach for such study would be developing an app or web site that can be accessed by patients, where they can ask free text questions that a predefined rule-based chat-bot will address. During the study there will be a doctor monitoring the collected data and improving the rules and the possible responses (and also provide new responses when the workflow has reached a dead-end), we do plan to collect the conversations and apply machine learning to generate improved work-flow tree (and questions) over time, however the plan is to do any data analysis and processing offline, there is no intention of building a full product.
This is a low budget academy study, and the PHD student has good development skills and data science knowledge (python) and will be accompanied by a fellow student that will work on the engineering side. One of the conversational-AI options recommended for data scientists was RASA.
I invested the last few days reading and playing with several chat-bots solutions: RASA, Botpress, also looked at Dialogflow and read tons of comparison material which makes it more challenging.
From the sources on the internet it seems that RASA might be a better fit for data science projects, however it would be great to get a sense of the real learning curve and how fast one can expect to have a working bot, and the especially one that has to continuously update the rules.
Few things to clarify, We do have data to generate the questions and in touch with doctors to improve the quality, it seems that we need a way to introduce participants with multiple choices and provide answers (not just free text), being in the research side there is also no need to align with any specific big provider (i.e. Google, Amazon or Microsoft) unless it has a benefit, the important consideration are time, money and felxability, we would like to have a working approach in few weeks (and continuously improve it) the whole experiment will run for no more than 3-4 months. We do need to be able to extract all the data. We are not sure about which channel is best for such study WhatsApp? Website? Other? and what are the involved complexities?
Any thoughts about the challenges and considerations about dealing with chat-bots would be valuable.
I'm looking into designing a software platform that will aid linguists and anthropologists in their study of previously unstudied languages. Statistics show that around 1,000 languages exist that have never been studied by a person outside of their respective speaker groups.
My goal is to utilize TensorFlow to make a platform that will allow linguists to study and document these languages more efficiently, and to help them create written systems for the ones that don't have a written system already. One of their current methods of accomplishing such a task is three-fold: 1) Record a native speaker conversing in the language, 2) Listening to that recording and trying to transcribe it into the IPA, 3) From the phonetics, analyzing the phonemics and phonotactics of the language to eventually create a written system for the speaker.
My proposed platform would cut that research time down from a minimum of a year to a maximum of six months. Before I start, I have some questions...
What would be required to train TensorFlow to transcribe live audio into the IPA? Has this already been done? and if so, how would I utilize a previous solution for this project? Is a project like this even possible with TensorFlow? if not, what would you recommend using instead?
My apologies for the magnitude of this question. I don't have much experience in the realm of machine learning, as I am just beginning the research process for this project. Any help is appreciated!
I guess I will take a first shot at answering this. Since the question is pretty general, my answer will have to be pretty general as well.
What would be required. At the very least you would have to have a large dataset of pre-transcribed data. Ideally a large amount of spoken language audio mapped to characters in the phonetic alphabet, so the system could learn the sound of individual characters rather than whole transcribed words. If such a dataset doesn't exist, a less granular dataset could be used, mapping single words to their transcriptions. Then you would need a model, that is the actual neural network architecture implemented in code. And lastly you would need some computing resources. This is not something you can train casually, you would either have to buy some time in a cloud based machine learning framework (like Google Cloud ML) or build a fairly expensive machine to train at home.
Has this been done? I don't know. I don't think so. There have been published papers reporting various degrees of success at training systems to transcribe speech. Here is one, for example, http://deeplearning.stanford.edu/lexfree/lexfree.pdf It seems that since the alphabet you want to transcribe to is specifically designed to capture the way words sound rather than just write down the words you might have more success at training such a model.
Is it possible with TensorFlow. Yes, most likely. TensorFlow is well suited for implementing most modern deep learning architectures. Unless you end up designing some really weird and very original model for this purpose, TensorFlow should work just fine.
Edit: after some thought in part 1, you would have to use a dataset mapping spoken words to their transcriptions, since I expect that the same sound pronounced separately would be different from when the same sound is used in a word.
This has actually been done, albeit in PyTorch, by a group at CMU: https://github.com/xinjli/allosaurus
My fellow researchers and I have been using SUMO for a bit of time now, and have been helped greatly by the informative posts here in the past - so I just wanted to share some appreciation ahead of time :)
My question is: Is it possible to append a Hybrid Vehicle model in the PHEM program files seen below?:
https://github.com/planetsumo/sumo/tree/master/sumo/src/utils/emissions
Ideally, this hybrid vehicle would be able to include the State of Charge (SOC) control and include the temporal changes of SOC as the vehicle travels according to the drive cycle, just as there are temporal changes to Fuel Consumption, emissions, etc. If possible, we would hope to create a new column for SOC information in the emissionsDrivingCycle output cited here: http://sumo.dlr.de/wiki/Tools/Emissions#emissionsDrivingCycle
Our team was thinking that it would be great to use the emissionsDrivingCycle tool with this new vehicle type, as we could use the standard vehicle definitions in PHEMLight, and define traffic in the standard ways. Essentially we are wondering: 1) Is it feasible to implement a Hybrid Vehicle energy balance model in the PHEM files, and 2) Could these files then be compiled to form a new version of the existing emissionsDrivingCycle tool?
Before we started to play around too much, we thought it would be best to ask the group first to see what the community might have to say.
Thank you all once again!
Regards,
Van
First of all, there are several PHEMlight emission types available but there is currently no hybrid. The problem with hybrids is, that you need essentially to change the emission model while the vehicle is running from combustion to electric (and vice versa) and there may be very different strategies in place for doing so. I would propose to make this change rather explicit than hiding it in an additional emission model file which can do nothing but average over all the different strategies out there. So you can switch emission models in a running simulation simply by giving a new vehicle type to an existing vehicle using TraCI. In Python this would be
traci.vehicle.setType.
I am a PhD student in translation studies and I am currently working on my dissertation. I am using LSA Similarity interface as a method of analysis in my dissertation. My background is in linguistics and not computer science. I tried to find an easy LSA document categorisation tool but I could not find any. I tried to play with Gensim, I did not work. I think my problem is with linking my corpus (txt files) with the Gensim tool to do the analysis (I don't know how o do this step). I would greatly appreciate if anyone could help me with the analysis or direct me to any tool or easy tutorials to do it using Gensim.
I want to do the following: I want to apply document-doecument queries to retrieve the most relevant 5 documents from the corpus to the query document.
I have 15 query document
I have one corpus of (150 texts)The texts are short stories
I am desperate and I was hesitant to post this question here. I am sure that applying LSA in translation studies would add to the field and this makes me more persistent to find a way to do my analysis.
The only really easy, user-friendly tool for LSA that is out there right now is http://lsa.colorado.edu/ . Unfortunately, it is a web-based tool only, and it does not allow you to train LSA on your own corpora. But depending on your needs, that may not matter.
If I'm understanding you correctly, you need document-document similarity scores between each of 15 query documents and each of 150 short stories (a total of 15*150=2250 similarity scores). If these query documents and short stories are in English, then you can use the version of LSA that is trained on the TASA corpus used in many studies of LSA as follows:
Go to http://lsa.colorado.edu/
Select One-To-Many Comparison
Copy-paste one of the short stories in the "Main text" box, and the 15 queries separated with a blank line in the "Texts to compare" box
Repeat for each of your short stories. A huge pain? Yes. But if you are desperate...
If you program a little bit in Python or R, other tools for LSA include http://clic.cimec.unitn.it/composes/toolkit/introduction.html and http://cran.r-project.org/web/packages/lsa/lsa.pdf , and would save you the manual labor of the above suggestion. Also, I know you already tried Gensim, but there is a nice tutorial for it at http://radimrehurek.com/gensim/tutorial.html that you might try following if you haven't already.
I am looking for practical problem (or implementations, applications) examples which are effectively algoritmized using swarm intelligence. I found that multicriteria optimization is one example. Are there any others?
IMHO swarm-intelligence should be added to the tags
Are you looking for toy problems or more for real-world applications?
In the latter category I know variants on swarm intelligence algorithms are used in Hollywood for CGI animations such as large (animated) armies riding the fields of battle.
Related but more towards the toy-problem end of the spectrum you can model large crowds with similar algorithms, and use it for example to simulate disaster-scenarios. AFAIK the Dutch institute TNO has research groups on this topic, though I couldn't find an English link just by googling.
One suggestion for a place to start further investigation would be this PDF book:
http://www.cs.vu.nl/~schut/dbldot/collectivae/sci/sci.pdf
That book also has an appendix (B) with some sample projects you could try and work on.
If you want to get a head start there are several frameworks (scientific use) for multi-agent systems such as swarming intelligence (most of 'em are written with Java I think). Some of them include sample apps too. For example have a look at these:
Repast:
http://repast.sourceforge.net/repast_3/
Swarm.org:
http://swarm.org/
Netlogo:
http://ccl.northwestern.edu/netlogo
Post edited, added more info.
I will take your question like: what kind of real-world problems SI can solve?
There are alot. Swarm intelligence is based on the complex behaviour of swarms, where agents in the swarm coordinate and cooperate by executing very simple rules to generate an emergent complex auto organized behaviour. Also, the agents often make a deliberation process to make efficient decisions, and also, the emergent behaviour of the swarms allows them to find patterns, learn and adapt to their environment. Therefore, real-world applications based on SI are those that often required coordination and cooperation techniques, optimization process, exploratory analysis, dynamical poblems, etc. Some of these are:
Optimization techniques (mathematical functions for example)
Coordination of a swarm of robots (to organize inventory for example)
Routing in communication networks. (This is also dynamical combinatorial optimization)
Data analysis (usually exploratory, like clustering). SI has alot of applications in data mining and machine learning. This allows SI algorithms to find interesting patterns in big sets of data.
Np problems in general
I'm sure there are alot more. You should check the book:
"Swarm Intelligence: from natural to artificial systems". This is the basic book.
Take care.