Sentiment Analysis

This tutorial is available as an IPython notebook at Malaya/example/sentiment.

%%time
import malaya

CPU times: user 4.96 s, sys: 655 ms, total: 5.61 s
Wall time: 4.7 s

string1 = 'Sis, students from overseas were brought back because they are not in their countries which is if something happens to them, its not the other countries’ responsibility. Student dalam malaysia ni dah dlm tggjawab kerajaan. Mana part yg tak faham?'
string2 = 'Harap kerajaan tak bukak serentak. Slowly release week by week. Focus on economy related industries dulu'

Load multinomial model

All model interface will follow sklearn interface started v3.4,

model.predict(List[str])

model.predict_proba(List[str])

model = malaya.sentiment.multinomial()

model.predict([string1, string2])

['positive', 'negative']

model.predict_proba([string1, string2])

[{'negative': 0.008184343650433397,
  'positive': 0.18156563495665812,
  'neutral': 0.8102500213929085},
 {'negative': 0.010056240383248257,
  'positive': 0.009943759616751778,
  'neutral': 0.98}]

Disable neutral probability,

model.predict_proba([string1, string2], add_neutral = False)

[{'negative': 0.40921718252166983, 'positive': 0.5907828174783291},
 {'negative': 0.5028120191624128, 'positive': 0.49718798083758886}]

List available Transformer models

malaya.sentiment.available_transformer()

	Size (MB)	Accuracy
bert	425.6	0.993
tiny-bert	57.4	0.987
albert	48.6	0.992
tiny-albert	22.4	0.985
xlnet	446.6	0.993
alxlnet	46.8	0.991

Make sure you can check accuracy chart from here first before select a model, https://malaya.readthedocs.io/en/latest/Accuracy.html#sentiment-analysis

You might want to use Tiny-Albert, a very small size, 22.4MB, but the accuracy is still on the top notch.

Load XLNET model

All model interface will follow sklearn interface started v3.4,

model.predict(List[str])

model.predict_proba(List[str])

model = malaya.sentiment.transformer(model = 'xlnet')

WARNING:tensorflow:From /Users/huseinzolkepli/Documents/Malaya/malaya/function/__init__.py:54: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.

WARNING:tensorflow:From /Users/huseinzolkepli/Documents/Malaya/malaya/function/__init__.py:55: The name tf.GraphDef is deprecated. Please use tf.compat.v1.GraphDef instead.

WARNING:tensorflow:From /Users/huseinzolkepli/Documents/Malaya/malaya/function/__init__.py:49: The name tf.InteractiveSession is deprecated. Please use tf.compat.v1.InteractiveSession instead.

Predict batch of strings

model.predict_proba([string1, string2])

[{'negative': 0.00018888633, 'positive': 0.9811114, 'neutral': 0.018699706},
 {'negative': 0.8079505, 'positive': 0.0019204962, 'neutral': 0.19012898}]

model.predict_proba([string1, string2], add_neutral = False)

[{'negative': 0.029847767, 'positive': 0.97015226},
 {'negative': 0.1034979, 'positive': 0.89650214}]

Open emotion visualization dashboard

Default when you call predict_words it will open a browser with visualization dashboard, you can disable by visualization=False.

model.predict_words(string1)

from IPython.core.display import Image, display

display(Image('sentiment-dashboard.png', width=800))

Stacking models

More information, you can read at https://malaya.readthedocs.io/en/latest/Stack.html

multinomial = malaya.sentiment.multinomial()
alxlnet = malaya.sentiment.transformer(model = 'alxlnet')

malaya.stack.predict_stack([multinomial, alxlnet, model], [string1, string2])

[{'negative': 0.0005453552136673502,
  'positive': 0.5603020846001405,
  'neutral': 0.05399025419995675},
 {'negative': 0.0002248290781177622,
  'positive': 0.21361579430243546,
  'neutral': 0.022142383292097452}]

If you do not want neutral in predict_stack, simply override the parameter,

malaya.stack.predict_stack([multinomial, alxlnet, model], [string1, string2], add_neutral = False)

[{'negative': 0.05828375571937787, 'positive': 0.8221586003437801},
 {'negative': 0.014352668987571138, 'positive': 0.7835866999009022}]