#Sentiment Analysis

Sentiment analysis detects the emotions of the author contained in the text. Was the author

• happy (I loved it.),
• neutral (We went to London.),
• unhappy (The lunch was not good at all.), or
• ambivalent (The lunch was good, but expensive.)

about their experience?

You can detect sentiment of reviews, feedback or customer service inquiries. Analysing sentiment of news articles usually does not make much sense (with the exception of opinions), because sentiment analysis does not aim at distinguishing bad news from good news, but the opinion the author expresses about something.

Our sentiment analysis is driven by practical needs, not academic purity.

##Textual sentiment

Since it is often hard to express sentiment as a single number, we actually give you three numbers:

• mean - the average sentiment
• pos - the positive sentiment of the text (ignoring its negative sentiment)
• neg - the negative sentiment of the text (ignoring its positive sentiment)

{
    "language": {"detected": "en"},
    "docSentiment": {
        "mean": 0.6,
        "label": "positive",
        "positive": 0.8,
        "negative": -0.2
    },
    "usedChars": 100
}

##Item sentiment

In addition to sentiment of text, we can return so-called item sentiment – sentiment related to entities or relations. Note that at this moment, the sentiment of items is derived from the sentiment of sentences the items occur in. That obviously has some implication. For example, a positively judged entity in an sentence which is negative overall will be assigned a negative sentiment. Therefore actor would be assigned negative sentiment in the following sentence Even the talented actor could not make up for the absolutely disastrous script.

##Sample call

You can easily try it yourself:

def callGeneea(input):
    url = 'https://api.geneea.com/v3/analysis'
    headers = {
        'content-type': 'application/json',
        'Authorization': 'user_key '
    }

    return requests.post(url, json=input, headers=headers).json()

responseObj = callGeneea({
    'id': '1',
    'text': 'The trip to London was amazing. Only the food was weird. Especially the pizza was terrible.',
    'analyses': ['sentiment', 'entities', 'relations'],
    'returnItemSentiment': True,
    'domain': 'voc-hospitality'
)

print(responseObj)

You should get the following response:

{
    "id": "1",
    "language": {"detected": "en"},
    "entities": [
        {"id": "E0", "gkbId": "HSP-1091", "stdForm": "pizza", "type": "food"}
    ],
    "relations": [
        {"id": "R0", "name": "amazing", "textRepr": "amazing(trip)", "type": "ATTR", "args": [{"type": "SUBJECT", "name": "trip"}], "feats": {"negated": "false", "modality": ""}},
        {"id": "R1", "name": "weird", "textRepr": "weird(food)", "type": "ATTR", "args": [{"type": "SUBJECT", "name": "food"}], "feats": {"negated": "false", "modality": ""}},
        {"id": "R2", "name": "terrible", "textRepr": "terrible(pizza)", "type": "ATTR", "args": [{"type": "SUBJECT", "name": "pizza", "entityId": "E0"}], "feats": {"negated": "false", "modality": ""}}
    ],
    "docSentiment": {"mean": -0.1, "label": "negative", "positive": 0.2, "negative": -0.3},
    "itemSentiments": {
        "E0": {"mean": -0.5, "label": "negative", "positive": 0.0, "negative": -0.5},
        "R0": {"mean": 0.5, "label": "positive", "positive": 0.5, "negative": 0.0},
        "R1": {"mean": -0.4, "label": "negative", "positive": 0.0, "negative": -0.4},
        "R2": {"mean": -0.5, "label": "negative", "positive": 0.0, "negative": -0.5}
    },
    "usedChars": 100
}

You can see that

• the whole document is judged as slighly negative
  (“docSentiment”: {“mean”: -0.1, “label”: “negative”, …}),
• with some positive
  (“docSentiment”: { … “positive”: 0.2 … })
  and some negative aspects
  (“docSentiment”: { … “negative”: -0.3})
• pizza is judged as negative
  (“E0”: {“mean”: -0.5, “label”: “negative”, “positive”: 0.0, “negative”: -0.5})
• amazing trip is judged as positive
  (“R0”: {“mean”: 0.5, “label”: “positive”, “positive”: 0.5, “negative”: 0.0})
• weird food is judged as negative
  (“R1”: {“mean”: -0.4, “label”: “negative”, “positive”: 0.0, “negative”: -0.4})
• terrible pizza is judged as negative
  (“R2”: {“mean”: -0.5, “label”: “negative”, “positive”: 0.0, “negative”: -0.5})