Text Analytics
構造化されていないテキスト内のセンチメント分析、エンティティ、関係、キー フレーズなどの分析情報を得られるテキスト マイニング AI サービス
テキストからの分析情報のマイニング
Mine insights in unstructured text using natural language processing (NLP)—no machine learning expertise required. Gain a deeper understanding of customer opinions with sentiment analysis. Identify key phrases and entities such as people, places, and organizations to understand common topics and trends. Classify medical terminology using domain-specific, pretrained models. Evaluate text in a wide range of languages.
広範なエンティティの抽出
Identify important concepts in text, including key phrases and named entities such as people, events, and organizations.
強力な感情分析
Examine what customers are saying about your brand and analyze sentiments around specific topics through opinion mining.
Question answering
Get answers to questions from content such as FAQ, product manuals, blogs, and policies.
柔軟性の高いデプロイ
Run Text Analytics anywhere—in the cloud, on-premises, or at the edge in containers.
| 言語:: | English (信頼度: 100%) |
|---|---|
| キー フレーズ: | The Sirloin steak, Contoso Steakhouse, midtown NYC, dinner party, great menu, chief cook, John Doe, online menu, marvelous food, spot, owner, name, kitchen, dining, place, contososteakhouse, email, order, complaint |
| センチメント: |
ドキュメント
MIXED
86%
肯定的
0%
中立
14%
否定的
文 1
POSITIVE
99%
肯定的
1%
中立
0%
否定的
文 2
POSITIVE
100%
肯定的
0%
中立
0%
否定的
文 3
POSITIVE
100%
肯定的
0%
中立
0%
否定的
文 4
POSITIVE
100%
肯定的
0%
中立
0%
否定的
文 5
POSITIVE
100%
肯定的
0%
中立
0%
否定的
文 6
NEUTRAL
0%
肯定的
100%
中立
0%
否定的
文 7
NEGATIVE
0%
肯定的
0%
中立
100%
否定的
文 8
POSITIVE
100%
肯定的
0%
中立
0%
否定的
|
| 名前付きエンティティ: | Contoso Steakhouse [Location] midtown [Location-GPE] NYC [Location-GPE] last week [DateTime-DateRange] dinner party [Event] chief cook [PersonType] owner [PersonType] John Doe [Person] kitchen [Location-Structural] Sirloin steak [Product] www.contososteakhouse.com [URL] 312-555-0176 [Phone Number] order@contososteakhouse.com [Email] food [Product] |
| PII エンティティ: |
Type: Organization Value: Contoso Type: DateTime Value: last week Type: PersonType Value: chief cook Type: PersonType Value: owner Type: Person Value: John Doe Type: Phone Number Value: 312-555-0176 Type: Email Value: order@contososteakhouse.com Type: Organization Value: contososteakhouse |
| リンクされたエンティティ: | We went to Contoso Steakhouse located at midtown NYC last week for a dinner party, and we adore the spot! They provide marvelous food and they have a great menu. The chief cook happens to be the owner (I think his name is John Doe) and he is super nice, coming out of the kitchen and greeted us all. We enjoyed very much dining in the place! The Sirloin steak I ordered was tender and juicy, and the place was impeccably clean. You can even pre-order from their online menu at www.contososteakhouse.com, call 312-555-0176 or send email to order@contososteakhouse.com! The only complaint I have is the food didn't come fast enough. Overall I highly recommend it! |
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重要な概念を特定して分類する
Extract a broad range of prebuilt entities such as people, places, organizations, dates/times, numerals, and over 100 types of personally identifiable information (PII), including protected health information (PHI), in documents using named entity recognition.
非構造化テキストでキー フレーズを抽出
Quickly evaluate and identify the main points in unstructured text. Get a list of relevant phrases that best describe the subject of each record using key phrase extraction. Easily organize information to make sense of important topics and trends.
顧客の認識について理解を深める
Analyze positive and negative sentiment in social media, customer reviews, and other sources to get a pulse on your brand. Use opinion mining to explore customers' perception of specific attributes of products or services in text.
構造化されていない医療データを処理する
Extract insights from unstructured clinical documents such as doctors' notes, electronic health records, and patient intake forms using the health feature of Text Analytics in preview. Recognize, classify, and determine relationships between medical concepts such as diagnosis, symptoms, and dosage and frequency of medication.
Create a conversational layer over your data
Get answers to questions from semi-structured and unstructured content such as URLs, FAQ, product manuals, blogs, support documents, and more.
Deploy anywhere, in the cloud or at the edge
Run Text Analytics wherever your data resides. Build applications that are optimized for both robust cloud capabilities and edge locality using containers.
包括的なプライバシーとセキュリティ
- Your data stays yours. Microsoft doesn't use the training performed on your text to improve models.
- Cognitive Services がコンテナーを使用してデータを処理する場所を選択します。
- Azure インフラストラクチャを基盤とする Text Analytics は、エンタープライズ級のセキュリティ、可用性、コンプライアンス、管理性を提供します。
柔軟な価格で必要な機能を利用し、制御し、カスタマイズする
- Pay as you go based on the number of transactions, with no upfront costs.
Text Analytics のリソースとドキュメント
広く使われている開発者リソースを確認する
あらゆる規模の企業から寄せられる信頼
KPMG による不正行為の分析を効率化
KPMG は、Text Analytics を使用してパターンとキーワードを検出してコンプライアンス リスクにフラグを立てる Customer Risk Analytics ソリューションを使用して、金融機関のコンプライアンス コストを数百万ドルも節約できるように支援しています。
Wilson Allen は非構造化データから分析情報を見つける
Wilson Allen は、以前はサイロ化され構造化されていなかったデータから、これまでにないレベルの分析情報を見つけることができる強力な AI ソリューションを、世界中の法律事務所やプロフェッショナル サービス会社に提供しています。
IHC はサービス エンジニアを支援
Royal IHC は、Azure Cognitive Search と Text Analytics を使用して、さまざまなソースでの時間のかかる手動のデータ検索からエンジニアを解放し、構造化データと非構造化データの分析情報を提供します。
LaLiga はファンとのつながりを深める
LaLiga is engaging millions of fans around the world with a personal digital assistant, using Text Analytics to process incoming queries and determine user intent in multiple languages.
TIBCO が根本原因の分析をエッジで実現
TIBCO では、Text Analytics と Anomaly Detector を活用して、データ パターンの突然の変化などの異常を検知、分析し、根本原因を発見し、また推奨アクションを提供しています。
Kotak Mahindra Bank は生産性を加速
Kotak Asset Management は、件名、顧客情報、メールの内容をチャットボットが簡単に分析してセンチメントを特定し、次の最適なアクションをトリガーできるようにしすることで、顧客サービス管理を変革します。
Text Analytics に関してよく寄せられる質問
-
Text Analytics detects a wide range of languages, variants, and dialects. See the language support documentation for more information.
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Yes. Sentiment analysis and key phrase extraction are available for a select number of languages, and you may request additional languages in the Text Analytics Forum.
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キー フレーズ抽出では、重要でない単語や独立した形容詞が除かれます。"spectacular views" (すばらしい眺め) や "foggy weather" (霧の多い天気) のような形容詞と名詞の組み合わせはまとめて返されます。一般的に、出力は文の名詞と目的語で構成され、重要度の高い順に列挙されます。重要度は、特定の概念が言及された回数、またはテキストのその要素と他の要素の関係性で計測されます。
-
モデルやアルゴリズムの改良は、変更が大きい場合は発表され、変更が小さい場合はサービスに追加されます。ある程度の時間が経過すると、同じテキストを入力したのに、センチメント スコアやキー フレーズ出力が異なることがあります。クラウドで管理されている機械学習リソースを利用している結果であり、異常ではありません。
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Yes, you can use the analyze operation in preview to combine more than one Text Analytics feature in the same asynchronous call. The analyze operation is currently only available in the Standard pricing tier and follows the same pricing criteria.