我如何构建一个模型来区分关于苹果(Inc。)的 tweet 和关于苹果(水果)的 tweet？

I would do it as follows:

1. Split the sentence into words, normalise them, build a dictionary
2. With each word, store how many times they occurred in tweets about the company, and how many times they appeared in tweets about the fruit - these tweets must be confirmed by a human
3. When a new tweet comes in, find every word in the tweet in the dictionary, calculate a weighted score - words that are used frequently in relation to the company would get a high company score, and vice versa; words used rarely, or used with both the company and the fruit, would not have much of a score.

Thank you for the comments thus far. Here is a working solution I prepared with PHP. I'd still be interested in hearing from others a more algorithmic approach to this same solution.

<?php


// Confusion Matrix Init
$tp = 0;
$fp = 0;
$fn = 0;
$tn = 0;
$arrFP = array();
$arrFN = array();


// Load All Tweets to string
$ch = curl_init();
curl_setopt($ch, CURLOPT_URL, 'http://pastebin.com/raw.php?i=m6pP8ctM');
curl_setopt($ch, CURLOPT_RETURNTRANSFER, 1);
$strCorpus = curl_exec($ch);
curl_close($ch);


// Load Tweets as Array
$arrCorpus = explode("\n", $strCorpus);
foreach ($arrCorpus as $k => $v) {
// init
$blnActualClass = substr($v,0,1);
$strTweet = trim(substr($v,2));


// Score Tweet
$intScore = score($strTweet);


// Build Confusion Matrix and Log False Positives & Negatives for Review
if ($intScore > 0) {
if ($blnActualClass == 1) {
// True Positive
$tp++;
} else {
// False Positive
$fp++;
$arrFP[] = $strTweet;
}
} else {
if ($blnActualClass == 1) {
// False Negative
$fn++;
$arrFN[] = $strTweet;
} else {
// True Negative
$tn++;
}
}
}


// Confusion Matrix and Logging
echo "
Predicted
1     0
Actual 1   $tp     $fp
Actual 0    $fn    $tn


";


if (count($arrFP) > 0) {
echo "\n\nFalse Positives\n";
foreach ($arrFP as $strTweet) {
echo "$strTweet\n";
}
}


if (count($arrFN) > 0) {
echo "\n\nFalse Negatives\n";
foreach ($arrFN as $strTweet) {
echo "$strTweet\n";
}
}


function LoadDictionaryArray() {
$strDictionary = <<<EOD
10|iTunes
10|ios 7
10|ios7
10|iPhone
10|apple inc
10|apple corp
10|apple.com
10|MacBook
10|desk top
10|desktop
1|config
1|facebook
1|snapchat
1|intel
1|investor
1|news
1|labs
1|gadget
1|apple store
1|microsoft
1|android
1|bonds
1|Corp.tax
1|macs
-1|pie
-1|clientes
-1|green apple
-1|banana
-10|apple pie
EOD;


$arrDictionary = explode("\n", $strDictionary);
foreach ($arrDictionary as $k => $v) {
$arr = explode('|', $v);
$arrDictionary[$k] = array('value' => $arr[0], 'term' => strtolower(trim($arr[1])));
}
return $arrDictionary;
}


function score($str) {
$str = strtolower($str);
$intScore = 0;
foreach (LoadDictionaryArray() as $arrDictionaryItem) {
if (strpos($str,$arrDictionaryItem['term']) !== false) {
$intScore += $arrDictionaryItem['value'];
}
}
return $intScore;
}
?>

The above outputs:

           Predicted
1     0
Actual 1   31     1
Actual 0    1    17




False Positives
1|Royals apple #ASGame @mlb @ News Corp Building http://instagram.com/p/bBzzgMrrIV/




False Negatives
-1|RT @MaxFreixenet: Apple no tiene clientes. Tiene FANS// error.... PAGAS por productos y apps, ergo: ERES CLIENTE.

What you are looking for is called Named Entity Recognition. It is a statistical technique that (most commonly) uses Conditional Random Fields to find named entities, based on having been trained to learn things about named entities.

Essentially, it looks at the content and context of the word, (looking back and forward a few words), to estimate the probability that the word is a named entity.

Good software can look at other features of words, such as their length or shape (like "Vcv" if it starts with "Vowel-consonant-vowel")

A very good library (GPL) is Stanford's NER

Here's the demo: http://nlp.stanford.edu:8080/ner/

Some sample text to try:

I was eating an apple over at Apple headquarters and I thought about Apple Martin, the daughter of the Coldplay guy

(the 3class and 4class classifiers get it right)

In all the examples that you gave, Apple(inc) was either referred to as Apple or apple inc, so a possible way could be to search for:

• a capital "A" in Apple

• an "inc" after apple

• words/phrases like "OS", "operating system", "Mac", "iPhone", ...

• or a combination of them

If you don't have an issue using an outside library, I'd recommend scikit-learn since it can probably do this better & faster than anything you could code by yourself. I'd just do something like this:

Build your corpus. I did the list comprehensions for clarity, but depending on how your data is stored you might need to do different things:

def corpus_builder(apple_inc_tweets, apple_fruit_tweets):
corpus = [tweet for tweet in apple_inc_tweets] + [tweet for tweet in apple_fruit_tweets]
labels = [1 for x in xrange(len(apple_inc_tweets))] + [0 for x in xrange(len(apple_fruit_tweets))]
return (corpus, labels)

The important thing is you end up with two lists that look like this:

([['apple inc tweet i love ios and iphones'], ['apple iphones are great'], ['apple fruit tweet i love pie'], ['apple pie is great']], [1, 1, 0, 0])

The [1, 1, 0, 0] represent the positive and negative labels.

Then, you create a Pipeline! Pipeline is a scikit-learn class that makes it easy to chain text processing steps together so you only have to call one object when training/predicting:

def train(corpus, labels)
pipe = Pipeline([('vect', CountVectorizer(ngram_range=(1, 3), stop_words='english')),
('tfidf', TfidfTransformer(norm='l2')),
('clf', LinearSVC()),])
pipe.fit_transform(corpus, labels)
return pipe

Inside the Pipeline there are three processing steps. The CountVectorizer tokenizes the words, splits them, counts them, and transforms the data into a sparse matrix. The TfidfTransformer is optional, and you might want to remove it depending on the accuracy rating (doing cross validation tests and a grid search for the best parameters is a bit involved, so I won't get into it here). The LinearSVC is a standard text classification algorithm.

Finally, you predict the category of tweets:

def predict(pipe, tweet):
prediction = pipe.predict([tweet])
return prediction

Again, the tweet needs to be in a list, so I assumed it was entering the function as a string.

Put all those into a class or whatever, and you're done. At least, with this very basic example.

I didn't test this code so it might not work if you just copy-paste, but if you want to use scikit-learn it should give you an idea of where to start.

EDIT: tried to explain the steps in more detail.

You can do the following:

1. Make a dict of words containing their count of occurrence in fruit and company related tweets. This can be achieved by feeding it some sample tweets whose inclination we know.

2. Using enough previous data, we can find out the probability of a word occurring in tweet about apple inc.

3. Multiply individual probabilities of words to get the probability of the whole tweet.

A simplified example:

p_f = Probability of fruit tweets.

p_w_f = Probability of a word occurring in a fruit tweet.

p_t_f = Combined probability of all words in tweet occurring a fruit tweet = p_w1_f * p_w2_f * ...

p_f_t = Probability of fruit given a particular tweet.

p_c, p_w_c, p_t_c, p_c_t are respective values for company.

A laplacian smoother of value 1 is added to eliminate the problem of zero frequency of new words which are not there in our database.

old_tweets = {'apple pie sweet potatoe cake baby https://vine.co/v/hzBaWVA3IE3': '0', ...}
known_words = {}
total_company_tweets = total_fruit_tweets =total_company_words = total_fruit_words = 0


for tweet in old_tweets:
company = old_tweets[tweet]
for word in tweet.lower().split(" "):
if not word in known_words:
known_words[word] = {"company":0, "fruit":0 }
if company == "1":
known_words[word]["company"] += 1
total_company_words += 1
else:
known_words[word]["fruit"] += 1
total_fruit_words += 1


if company == "1":
total_company_tweets += 1
else:
total_fruit_tweets += 1
total_tweets = len(old_tweets)


def predict_tweet(new_tweet,K=1):
p_f = (total_fruit_tweets+K)/(total_tweets+K*2)
p_c = (total_company_tweets+K)/(total_tweets+K*2)
new_words = new_tweet.lower().split(" ")


p_t_f = p_t_c = 1
for word in new_words:
try:
wordFound = known_words[word]
except KeyError:
wordFound = {'fruit':0,'company':0}
p_w_f = (wordFound['fruit']+K)/(total_fruit_words+K*(len(known_words)))
p_w_c = (wordFound['company']+K)/(total_company_words+K*(len(known_words)))
p_t_f *= p_w_f
p_t_c *= p_w_c


#Applying bayes rule
p_f_t = p_f * p_t_f/(p_t_f*p_f + p_t_c*p_c)
p_c_t = p_c * p_t_c/(p_t_f*p_f + p_t_c*p_c)
if p_c_t > p_f_t:
return "Company"
return "Fruit"

Make an AI filter to distinguish Apple Inc (the company) from apple (the fruit). Since these are tweets, define your training set with a vector of 140 fields, each field being the character written in the tweet at position X (0 to 139). If the tweet is shorter, just give a value for being blank.

Then build a training set big enough to get a good accuracy (subjective to your taste). Assign a result value to each tweet, a Apple Inc tweet get 1 (true) and an apple tweet (fruit) gets 0. It would be a case of supervised learning in a logistic regression.

That is machine learning, is generally easier to code and performs better. It has to learn from the set you give it, and it's not hardcoded.

I don't know Python, so I can not write the code for it, but if you were to take more time for machine learning's logic and theory you might want to look the class I'm following.

Try the Coursera course Machine Learning by Andrew Ng. You will learn machine learning on MATLAB or Octave, but once you get the basics you will be able to write machine learning in about any language if you do understand the simple math (simple in logistic regression).

That is, getting the code from someone won't make you able to understand what is going in the machine learning code. You might want to invest a couple of hours on the subject to see what is really going on.

There's a really good library for processing natural language text in Python called nltk. You should take a look at it.

One strategy you could try is to look at n-grams (groups of words) with the word "apple" in them. Some words are more likely to be used next to "apple" when talking about the fruit, others when talking about the company, and you can use those to classify tweets.

I have a semi-working system that solves this problem, open sourced using scikit-learn, with a series of blog posts describing what I'm doing. The problem I'm tackling is word-sense disambiguation (choosing one of multiple word sense options), which is not the same as Named Entity Recognition. My basic approach is somewhat-competitive with existing solutions and (crucially) is customisable.

There are some existing commercial NER tools (OpenCalais, DBPedia Spotlight, and AlchemyAPI) that might give you a good enough commercial result - do try these first!

I used some of these for a client project (I consult using NLP/ML in London), but I wasn't happy with their recall (precision and recall). Basically they can be precise (when they say "This is Apple Inc" they're typically correct), but with low recall (they rarely say "This is Apple Inc" even though to a human the tweet is obviously about Apple Inc). I figured it'd be an intellectually interesting exercise to build an open source version tailored to tweets. Here's the current code: https://github.com/ianozsvald/social_media_brand_disambiguator

I'll note - I'm not trying to solve the generalised word-sense disambiguation problem with this approach, just brand disambiguation (companies, people, etc.) when you already have their name. That's why I believe that this straightforward approach will work.

I started this six weeks ago, and it is written in Python 2.7 using scikit-learn. It uses a very basic approach. I vectorize using a binary count vectorizer (I only count whether a word appears, not how many times) with 1-3 n-grams. I don't scale with TF-IDF (TF-IDF is good when you have a variable document length; for me the tweets are only one or two sentences, and my testing results didn't show improvement with TF-IDF).

I use the basic tokenizer which is very basic but surprisingly useful. It ignores @ # (so you lose some context) and of course doesn't expand a URL. I then train using logistic regression, and it seems that this problem is somewhat linearly separable (lots of terms for one class don't exist for the other). Currently I'm avoiding any stemming/cleaning (I'm trying The Simplest Possible Thing That Might Work).

The code has a full README, and you should be able to ingest your tweets relatively easily and then follow my suggestions for testing.

This works for Apple as people don't eat or drink Apple computers, nor do we type or play with fruit, so the words are easily split to one category or the other. This condition may not hold when considering something like #definance for the TV show (where people also use #definance in relation to the Arab Spring, cricket matches, exam revision and a music band). Cleverer approaches may well be required here.

I have a series of blog posts describing this project including a one-hour presentation I gave at the BrightonPython usergroup (which turned into a shorter presentation for 140 people at DataScienceLondon).

If you use something like LogisticRegression (where you get a probability for each classification) you can pick only the confident classifications, and that way you can force high precision by trading against recall (so you get correct results, but fewer of them). You'll have to tune this to your system.

Here's a possible algorithmic approach using scikit-learn:

• Use a Binary CountVectorizer (I don't think term-counts in short messages add much information as most words occur only once)
• Start with a Decision Tree classifier. It'll have explainable performance (see Overfitting with a Decision Tree for an example).
• Move to logistic regression
• Investigate the errors generated by the classifiers (read the DecisionTree's exported output or look at the coefficients in LogisticRegression, work the mis-classified tweets back through the Vectorizer to see what the underlying Bag of Words representation looks like - there will be fewer tokens there than you started with in the raw tweet - are there enough for a classification?)
• Look at my example code in https://github.com/ianozsvald/social_media_brand_disambiguator/blob/master/learn1.py for a worked version of this approach

Things to consider:

• You need a larger dataset. I'm using 2000 labelled tweets (it took me five hours), and as a minimum you want a balanced set with >100 per class (see the overfitting note below)
• Improve the tokeniser (very easy with scikit-learn) to keep # @ in tokens, and maybe add a capitalised-brand detector (as user @user2425429 notes)
• Consider a non-linear classifier (like @oiez's suggestion above) when things get harder. Personally I found LinearSVC to do worse than logistic regression (but that may be due to the high-dimensional feature space that I've yet to reduce).
• A tweet-specific part of speech tagger (in my humble opinion not Standford's as @Neil suggests - it performs poorly on poor Twitter grammar in my experience)
• Once you have lots of tokens you'll probably want to do some dimensionality reduction (I've not tried this yet - see my blog post on LogisticRegression l1 l2 penalisation)

Re. overfitting. In my dataset with 2000 items I have a 10 minute snapshot from Twitter of 'apple' tweets. About 2/3 of the tweets are for Apple Inc, 1/3 for other-apple-uses. I pull out a balanced subset (about 584 rows I think) of each class and do five-fold cross validation for training.

Since I only have a 10 minute time-window I have many tweets about the same topic, and this is probably why my classifier does so well relative to existing tools - it will have overfit to the training features without generalising well (whereas the existing commercial tools perform worse on this snapshop, but more reliably across a wider set of data). I'll be expanding my time window to test this as a subsequent piece of work.

Using a decision tree seems to work quite well for this problem. At least it produces a higher accuracy than a naive bayes classifier with my chosen features.

If you want to play around with some possibilities, you can use the following code, which requires nltk to be installed. The nltk book is also freely available online, so you might want to read a bit about how all of this actually works: http://nltk.googlecode.com/svn/trunk/doc/book/ch06.html

#coding: utf-8
import nltk
import random
import re


def get_split_sets():
structured_dataset = get_dataset()
train_set = set(random.sample(structured_dataset, int(len(structured_dataset) * 0.7)))
test_set = [x for x in structured_dataset if x not in train_set]


train_set = [(tweet_features(x[1]), x[0]) for x in train_set]
test_set = [(tweet_features(x[1]), x[0]) for x in test_set]
return (train_set, test_set)


def check_accurracy(times=5):
s = 0
for _ in xrange(times):
train_set, test_set = get_split_sets()
c = nltk.classify.DecisionTreeClassifier.train(train_set)
# Uncomment to use a naive bayes classifier instead
#c = nltk.classify.NaiveBayesClassifier.train(train_set)
s += nltk.classify.accuracy(c, test_set)


return s / times




def remove_urls(tweet):
tweet = re.sub(r'http:\/\/[^ ]+', "", tweet)
tweet = re.sub(r'pic.twitter.com/[^ ]+', "", tweet)
return tweet


def tweet_features(tweet):
words = [x for x in nltk.tokenize.wordpunct_tokenize(remove_urls(tweet.lower())) if x.isalpha()]
features = dict()
for bigram in nltk.bigrams(words):
features["hasBigram(%s)" % ",".join(bigram)] = True
for trigram in nltk.trigrams(words):
features["hasTrigram(%s)" % ",".join(trigram)] = True
return features


def get_dataset():
dataset = """copy dataset in here
"""
structured_dataset = [('fruit' if x[0] == '0' else 'company', x[2:]) for x in dataset.splitlines()]
return structured_dataset


if __name__ == '__main__':
print check_accurracy()

To simplify answers based on Conditional Random Fields a bit...context is huge here. You will want to pick out in those tweets that clearly show Apple the company vs apple the fruit. Let me outline a list of features here that might be useful for you to start with. For more information look up noun phrase chunking, and something called BIO labels. See (http://www.cis.upenn.edu/~pereira/papers/crf.pdf)

Surrounding words: Build a feature vector for the previous word and the next word, or if you want more features perhaps the previous 2 and next 2 words. You don't want too many words in the model or it won't match the data very well. In Natural Language Processing, you are going to want to keep this as general as possible.

Other features to get from surrounding words include the following:

Whether the first character is a capital

Whether the last character in the word is a period

The part of speech of the word (Look up part of speech tagging)

The text itself of the word

I don't advise this, but to give more examples of features specifically for Apple:

WordIs(Apple)

NextWordIs(Inc.)

You get the point. Think of Named Entity Recognition as describing a sequence, and then using some math to tell a computer how to calculate that.

Keep in mind that natural language processing is a pipeline based system. Typically, you break things in to sentences, move to tokenization, then do part of speech tagging or even dependency parsing.

This is all to get you a list of features you can use in your model to identify what you're looking for.

Use LibShortText. This Python utility has already been tuned to work for short text categorization tasks, and it works well. The maximum you'll have to do is to write a loop to pick the best combination of flags. I used it to do supervised speech act classification in emails and the results were up to 95-97% accurate (during 5 fold cross validation!).

And it comes from the makers of LIBSVM and LIBLINEAR whose support vector machine (SVM) implementation is used in sklearn and cran, so you can be reasonably assured that their implementation is not buggy.

I would recommend avoiding answers suggesting entity recognition. Because this task is a text-classification first and entity recognition second (you can do it without the entity recognition at all).

I think the fastest path to results will be spacy + prodigy. Spacy has well thought through model for English language, so you don't have to build your own. While prodigy allows quickly create training datasets and fine tune spacy model for your needs.

If you have enough samples, you can have a decent model in 1 day.